International Journal of Business Intelligence and Data Mining (28 papers in press)

Regular Issues

• Analysis of online marketing user participation preference attribute based on social network text mining  
  by Lu Zhang, Wanqing Chen, Hengzhi Nie 
  Abstract: In order to improve the accuracy of online marketing users' participation preference feature attribute recognition, an analysis method of online marketing users' participation preference feature attribute based on social network text mining is proposed. Firstly, the TF-IDF algorithm is used to calculate the weight value of keywords in the tag, and then the user portrait of the social network platform is constructed after sorting. Then, the collaborative filtering algorithm is used to determine the user's preference characteristics for products containing keywords, and the K-L feature compressor is used to extract the user's participation preference characteristics of online marketing. Finally, the online marketing user participation preference characteristic attributes are classified to realize the analysis of online marketing user participation preference characteristic attributes. The experimental results show that the accuracy of this method is always above 90% and the average time is 3.88s.
  Keywords: social networks; text mining; online marketing; preferential features; TF-IDF algorithm.
  DOI: 10.1504/IJBIDM.2025.10067625
   
  

Special Issue on: Deep Learning Technology and Big Data Method for Business Intelligence and Management

• Learning from high-dimensional unlabelled data with outliers: a novel robust approach  
  by Abdul Wahid 
  Abstract: This paper investigates the problem of feature selection and classification under the presence of multivariate outliers in high-dimensional unlabeled data. The research question is how to identify outliers and deal with them in unsupervised learning to improve the clustering accuracy compared with the state-of-the-art non-robust feature selection techniques. For this purpose, a robust method is proposed by utilize the Mahalanobis distance for outlier identification based on the minimum regularized covariance determinants approach. Furthermore, a new weighting scheme based on Mahalanobis distance is developed for dealing with outlying data points. Finally, it is suggested to combine the proposed weight function and least squared loss function along with the graph and sparsity constraints for achieving the robustness. This new procedure is named Robust Self-Representation Sparse Reconstruction and Manifold Regularization (RSSRMR). The novel method is compared with previously proposed unsupervised feature select
  Keywords: clustering; high-dimensional data; feature selection; Mahalanobis distance; multivariate outliers.
  DOI: 10.1504/IJBIDM.2025.10066878
   
  
• Personalised recommendation method for smart library literature based on user behaviour feature perception  
  by Yina Liu 
  Abstract: To solve the problem that existing library literature recommendation methods cannot achieve diversity and personalisation, this study proposes a personalised recommendation method for smart library literature based on user behaviour feature perception technology. Firstly, based on network coding technology, the collection of user behaviour data for smart libraries is completed, and the collected data is reduced in dimensionality through information entropy to remove redundant features. Then, a user behaviour feature model is constructed through Bayesian networks to perceive and analyse user behaviour, obtain user behaviour features, and finally, based on the feature perception results, a collaborative filtering algorithm is used to complete personalised recommendation of literature materials in the smart library. The experimental results show that this method can fully utilise the behavioural characteristics of users, accurately understand their interests and needs, and provide more accurate literature recommendation results.
  Keywords: perception of user behaviour characteristics; smart library; literature materials; personalised recommendation; network coding; information entropy.
  DOI: 10.1504/IJBIDM.2025.10066987
   
  
• Method for mining students online English learning intention based on user portrait and big data  
  by Yanli Li, Lili Wang, Haitao Gao, Bin Zhang 
  Abstract: To overcome the problems of low recall, low accuracy, and long time in traditional methods, a new method for mining students' online English learning intention based on user portrait and big data is proposed. With the support of big data technology, the maximum mean difference algorithm is used to determine the distance between student online English learning data sample points, and the K-means algorithm is used to implement student online English learning data collection. The collected data is used to construct user personas, and the attention mechanism is used to extract students' online English learning characteristics. A student online English learning willingness mining model based on extreme learning machine network is established to obtain relevant mining results. Experimental tests have shown that the recall rate of the proposed method is always above 97.3%, the maximum mining accuracy is 98.1%, and the average mining time is 79.15ms.
  Keywords: user portrait; big data; students; online English; learning intention; maximum mean difference algorithm; attention mechanism; extreme learning machine.
  DOI: 10.1504/IJBIDM.2025.10066988
   
  
• Intelligent retrieval method for power grid dispatching information based on knowledge graph  
  by Baoyu Hou, Qichao Wang, Zhiguo Zhou 
  Abstract: To improve the retrieval efficiency of power grid dispatch information, the paper proposes an intelligent retrieval method based on knowledge graph. Firstly, after mining the terminology of power grid dispatch information, the entities and relationships in the power grid dispatch information are extracted to obtain a string of entity names in the terminology dictionary, achieving the design of the knowledge graph pattern layer for power grid dispatch information; Finally, the power grid dispatch information is embedded into a discrete Hamming space, and the nearest neighbour retrieval method is used in the embedded space to achieve intelligent retrieval of power grid dispatch information. The experimental results show that the intelligent retrieval accuracy of our method can reach 98.51%, the recall rate of our method can reach 98.32%, and the time consumption of our method is only 6.6 seconds. The retrieval efficiency of power grid dispatch information is relatively high.
  Keywords: knowledge graph; Hamming space; nearest neighbour retrieval; term dictionary tree.
  DOI: 10.1504/IJBIDM.2025.10066989
   
  
• A sentiment classification method for Weibo sensitive topic text based on multimodal features  
  by Manlin Li 
  Abstract: Due to the problem of reduced classification accuracy in traditional text sentiment classification methods, this paper proposes a Weibo sensitive topic text sentiment classification method based on multimodal features. Firstly, the bidirectional loop structure is introduced to improve the GRU model, and a BiGRU model is constructed for multimodal feature extraction and fusion of sensitive topics on Weibo. Secondly, by combining seed features, similar features, and residual features, a multimodal feature cluster is constructed to improve the accuracy of classification. Finally, the constructed multimodal feature clusters are input into the support vector machine model to complete sentiment classification of Weibo sensitive topic text. The experimental results show that compared with traditional methods, our method achieves higher accuracy in all emotion categories.
  Keywords: multimodal features; Weibo sensitive topics; text sentiment classification; BiGRU model; multimodal feature clusters.
  DOI: 10.1504/IJBIDM.2025.10066994
   
  
• An accurate and rapid pushing of marketing information based on multidimensional data mining  
  by Zhisheng Zhou, Bin Li 
  Abstract: In order to address the accuracy and recall issues in marketing information push, this study proposes a strategy based on multidimensional data mining to achieve accurate and efficient marketing information push. First of all, collect marketing information and build a push probability index system; Secondly, the analytic hierarchy process is used to calculate the weight of the marketing information push index; Finally, considering the product life cycle, data mining technology is used to obtain the stable and random purchase interest of active and inactive users, and marketing information is accurately and rapidly pushed through the above four dimensions. The research results show that after adopting this method, the accuracy of marketing information push reached 98.1%, the recall rate reached 96.9%, and user satisfaction also increased to 98.5%, significantly improving the overall effect of marketing information push and user satisfaction.
  Keywords: analytic hierarchy process; purchase interest; data mining techniques; multidimensional data mining; indicator weight.
  DOI: 10.1504/IJBIDM.2025.10066996
   
  
• A method for merging and classifying higher mathematics teaching resources based on density clustering algorithm  
  by Hejie Chang, Xing Lv 
  Abstract: To enhance the recall and accuracy of resource merging classification, this study introduces a merging classification technique rooted in density clustering algorithms. Initially, we gather data pertaining to higher mathematics teaching resources. Subsequently, we convert textual sentences into word-level representations, eliminating stop words and unnecessary high-frequency vocabulary. Leveraging LDA, we extract mathematical resource features, transforming words into computer- and model-recognisable vectorised forms. Next, we calculate the density and distance between samples to categorise them into distinct groups, employing density clustering algorithms for merging and classifying teaching resources. Experimental findings reveal that our method achieves a classification recall rate of 99.6% and an accuracy of 99.9%, thereby enhancing the quality and efficacy of higher mathematics education.
  Keywords: density clustering; merge and classify; advanced mathematics; teaching resources; resource allocation.
  DOI: 10.1504/IJBIDM.2025.10066997
   
  
• Research on engineering cost prediction based on GA-BP neural network  
  by Yan Wu, Sha Lan, Tingting Liu 
  Abstract: To improve the accuracy of engineering cost prediction and reduce prediction errors, an engineering cost prediction method based on GA-BP neural network is proposed in this paper. Comprehensive index system for engineering cost prediction is constructed, and qualitative indicators are discretized using the equal interval method. The qualitative indicators are transformed into quantitative indicators through scale assignment. The BP neural network error is obtained through gradient descent, and the GA algorithm is used to adjust the weights from the output layer to the hidden layer. Using the discretized qualitative indicators as input vectors and engineering cost as the output vector, a prediction model for engineering cost based on GA-BP neural network is built to obtain prediction results. Experimental results show that the proposed method has a prediction range of 2.41%, a residual mean range of 0.005~0.219, a recall rate fluctuating between 96.9% and 99.7%,and high prediction accuracy.
  Keywords: GA algorithm; BP neural network; engineering cost prediction; gradient descent.
  DOI: 10.1504/IJBIDM.2025.10066998
   
  
• Intelligent evaluation method for multimedia network public opinion decline period based on multi-divisional optimisation  
  by Xuefang Zhou 
  Abstract: In order to overcome the long data collection time, low accuracy in extracting features of public opinion decline, and low precision rate associated with traditional methods, a new intelligent evaluation method for multimedia network public opinion decline period based on multi-divisional optimization is proposed. A evaluation index system for intelligent evaluation of public opinion decline period is constructed, and index data is collected and processed. The multiple fractal dimensions of the index data are determined, and multi-divisional optimization is performed in conjunction with nonlinear support vector machines to extract features of public opinion decline. Public opinion decline period intelligent evaluation is achieved based on these features and the BiLSTM model. The experimental results show that the average data collection time of the proposed method is 0.72s, the average accuracy of feature extraction of public opinion decline is 97.66%, and the precision rate is consistently above 95%.
  Keywords: multi-divisional optimisation; multimedia network; public opinion decline period; intelligent evaluation; nonlinear support vector machine; BiLSTM model.
  DOI: 10.1504/IJBIDM.2025.10066999
   
  
• Data mining method for English classroom teaching quality based on hierarchical clustering  
  by Yue Zhang 
  Abstract: English classroom teaching involves multiple types of data, and effectively collecting and organising these data is a challenging task. Therefore, a hierarchical clustering based data mining method for English classroom teaching quality is proposed. Firstly, use dynamic layered distributed data collection algorithms to collect data; Secondly, use a moving average filter to smooth the data, transform the data through Fourier transform, and calculate the threshold for outliers based on normal distribution to achieve data outlier handling. Then, the recursive feature elimination method is used to perform feature selection on the data, and linear discriminant analysis is used to perform feature dimensionality reduction. Finally, use hierarchical clustering algorithm for data mining. The experimental results show that the recall rate of this method is high, the mean square error is low, the data storage space occupied is low, indicating that this method can effectively improve the effectiveness of teaching mining.
  Keywords: hierarchical clustering; data mining; recursive feature elimination; normal distribution.
  DOI: 10.1504/IJBIDM.2025.10067363
   
  
• A refined pushing method for financial product marketing data based on user interest mining  
  by Huijun Wang 
  Abstract: To improve the marketing effectiveness of financial products, the article designs a refined push method based on user interest mining. Divide user groups using K-means clustering algorithm and use density parameters to reflect user activity. Simultaneously, comprehensively explore users' interest and preferences in financial products. Build a marketing data model by calculating the transition probability of user browsing message categories. Finally, precise push is achieved by calculating the similarity between users, user interests, and candidate data. The experimental results show that after applying this method, the click through rate of financial products ranges from 93.58% to 96.69%, and the conversion rate of push results ranges from 0.88 to 0.95. The activity level of users participating in activities has always remained above 95%, verifying the effectiveness of this method.
  Keywords: Financial product marketing; Marketing data; User interests; Data push; K-means clustering; Category model.
  DOI: 10.1504/IJBIDM.2025.10067364
   
  
• Enhancing multiple document summarisation with DNETCNN and BCHOA techniques  
  by Mamatha Mandava, Surendra Reddy Vinta 
  Abstract: Multi-document summarising (MDS) is a helpful method for information aggregation that creates a clear and informative summary from a collection of papers linked to the same subject. Due to the significant number of information available online, it might be challenging to extract the needed information from an internet source these days. To generate the summary, we propose the binary chimp optimisation algorithm (BChOA) in this research. Several preprocessing techniques utilised to remove unwanted terms from the content. Then, for word embedding, FastText is used. The semantic and synthetic features are extracted using the DarkNet-53 and ConvNeXt methods. Using a darknet convolutional neural network (DNetCNN), the features derived from the syntactic and semantic features are concatenated. The Movie review dataset contains 2000 review files, and the BBC news dataset has 50 unique documents. Finally, the outcome demonstrates that our model compares to cutting-edge solutions in terms of semantics and syntactic structure.
  Keywords: multi-document summarisation; MDS; binary chimp optimisation algorithm; BChOA; ConvNeXt approach; darknet convolutional neural network; DNetCNN.
  DOI: 10.1504/IJBIDM.2025.10067365
   
  
• A precision marketing method for e-commerce considering the hidden behavioural characteristics of user online shopping  
  by Zheng Xu, Hengzhi Nie, Wanqing Chen 
  Abstract: In order to improve user order rate and achieve high marketing satisfaction, this article considers the hidden behaviour characteristics of user online shopping and designs an e-commerce precision marketing method. Firstly, use web crawler technology to collect and preprocess hidden behaviour data of e-commerce platform users during online shopping. Then, calculate the level of interest of e-commerce platform users in different labelled products during the online shopping process, and use natural language processing algorithms to identify the hidden behaviour characteristics of e-commerce platform users during online shopping. Based on the K-means clustering algorithm, perform fuzzy clustering on the hidden behaviour characteristics of online shopping. Finally, the Pearson similarity algorithm is used to calculate the similarity between feature data and target product data, and to construct an e-commerce platform’s online shopping product push matrix. Based on the ranking results of product push, precise e-commerce marketing is achieved. The experimental results show that using the proposed method, user satisfaction with product marketing recommendations remains above 85%, and user order rates remain above 90%. E-commerce marketing has high accuracy and good marketing effects.
  Keywords: e-commerce; electronic commerce; invisible behaviour; precision marketing; behavioural characteristics; fuzzy clustering.
  DOI: 10.1504/IJBIDM.2025.10067855
   
  

Special Issue on: Empowering Business Intelligence with AI Data Analytics and IoT for Efficient in Digital Era

• Presenting a model to reduce students' academic drop by using analytical comparison of machine learning algorithms in data mining (case study of Shahed University)  
  by Mozhdeh Salari, Reza Radfar, Mahdi Faghihi 
  Abstract: This research aims to find factors that predict undergraduate student educational performance. To achieve this goal, the study follows the CRISP-DM method. This study used various classification algorithms to predict the total GPA. The data used in this research are records of undergraduate students from 2012 in Shahed University. We used 1468 data records in data mining. We used the Rapidminer9.9 tool for modelling. This study also considers four feature selection techniques. This study used K-fold cross-validation to split the data. This study introduced the best model for predicting students' academic performance. In two-class modelling, we get better results and higher accuracy than four-class modelling. This research found the random forest algorithm best for predicting students performance. It achieved 94.17% accuracy with two classes. The random forest results show a higher chance of success in students with a higher 1st semester GPA.
  Keywords: student performance prediction; data mining; machine learning; data science applications in education.
  DOI: 10.1504/IJBIDM.2025.10067362
   
  

Special Issue on: Methods and Applications of Data Mining in Business Domains II

• Online teaching data distribution method based on learning behaviour big data mining  
  by Jing Chang 
  Abstract: To overcome the problems of low accuracy and recall of traditional online teaching data distribution methods, this paper proposes an online teaching data distribution method based on learning behaviour big data mining. Firstly, collect online teaching data and pre-process the distribution data; then, generate online teaching data distribution rules through triangular fuzzy clustering; finally, based on the learning behaviour big data mining method, the data is divided into fuzzy metrics, membership functions are established to update distribution rules, and big data mining is used to design data distribution schemes. The experimental results show that the distribution accuracy of our method can reach 99.89%, and the parameter recall rate can reach 97.89%. The actual results are in line with the expected results and have a good distribution effect.
  Keywords: learning behaviour; big data mining; online teaching; data distribution.
  DOI: 10.1504/IJBIDM.2025.10065173
   
  
• Enterprise financial risk early warning method based on PCA and SVM algorithms  
  by Yanyan Cao, Gechun Pei 
  Abstract: Aiming at the problems of low relevance and high false alarm rate of enterprise financial risk early warning, an enterprise financial risk early warning method based on PCA and SVM algorithm is proposed. Firstly, the sensitivity optimisation principal component analysis method is introduced, and the representative index is selected according to the threshold value to establish the index system. Then, support vector machine is introduced to store the data in the sample space, and the indicators are divided into positive and negative indicators. Finally, combined with FCM clustering algorithm, the early-warning decision function is constructed to realise the early-warning of enterprise financial risk. The experimental results show that the correlation of this method is higher than 0.915, the false alarm rate is lower than 2%, and the Matthews correlation coefficient is up to 1.00.
  Keywords: principal component analysis; PCA; support vector machine; SVM; corporate financial risks; risk warning; FCM clustering algorithm.
  DOI: 10.1504/IJBIDM.2025.10065576
   
  
• An English learning behaviour data mining based on improved ensemble learning algorithm  
  by Lin Fan, Pengqi Cao, Yunxia Du 
  Abstract: In order to enhance the learning effectiveness of English learners, this paper proposes an English learning behaviour data mining method based on improved ensemble learning algorithm. A web crawler is used to collect behavioural information of learners during the process of learning English, and learner profiles are constructed. The data is pre-processed, and collaborative filtering algorithms are employed to extract features of English learning behaviours. By treating English learning behaviour features as input vectors and data mining results as output vectors, an improved stacking ensemble learning model based on chain rules is constructed. This model is utilised to obtain data mining results for English learning behaviour. The experimental results show that the normalised difference accuracy of the proposed method is always above 90%, and the mAP value is always above 93%, indicating that the proposed method has high accuracy and good mining effect in English learning behaviour data mining.
  Keywords: ensemble learning; English learning; learning behaviour; data mining; chain rules; stacking ensemble learning model.
  DOI: 10.1504/IJBIDM.2025.10065187
   
  
• Web server log data pre-processing for mining zakat user profile using association rules  
  by Mohamad Farhan Mohamad Moshin, Wan Hussain Wan Ishak, Yuhanis Yusof, Jastini Mohd Jamil, Alwi Ahmad 
  Abstract: The internet’s transformative impact on businesses and marketing strategies underscores the pivotal role of websites in establishing credibility and disseminating information to customers. To measure website effectiveness, tracking visitor behaviour is essential. This study focuses on web log data from Lembaga Zakat Negeri Kedah (LZNK), a Malaysian government institution managing zakat which utilises web analytics and mining to gain insights into website usage. The objectives of this paper are two-fold: firstly, to detail the pre-processing of weblog data to ensure reliability for data mining. Secondly is to employ association rule mining to extract user patterns from pre-processed weblog data. To achieve this, the web logs were obtained from the LZNK’s website spanning from 2016 to November 2020 with a focus on user access in 2020. The findings reveal critical aspects of user behaviour including the most visited pages, popular page combinations, user interests, relationships between pages, and the impact of the entry page. Implementing these insights can enhance the LZNK website’s usability, user satisfaction, and highlighting the importance of adapting to evolving user preferences and technological advancements.
  Keywords: association rule; data pre-processing; user profile; web log; web mining.
  DOI: 10.1504/IJBIDM.2025.10065199
   
  
• Assessing ensemble techniques for imbalanced classification  
  by Eric P. Jiang 
  Abstract: Class imbalance represents a pervasive and challenging problem in machine learning and manifests in a wide range of real-world applications, where the distribution of data across different classes is highly skewed. Conventional machine learning algorithms tend to favour majority classes, often resulting in a failure to capture data patterns of minority classes. This bias can lead to undesirable outcomes in practice. This paper addresses the problem of class imbalance by conducting a comprehensive comparative study of various hybrid ensemble approaches that demonstrate promise in mitigating this learning issue. The study encompasses extensive experiments conducted on a diverse collection of datasets gathered from multiple application domains and characterised by a wide range of class imbalance ratios. To facilitate a comprehensive performance assessment of these methods in dealing with imbalanced data, we have deployed a combination of relevant and commonly used performance metrics and additionally, we have leveraged multiple non-parametric statistical tests to evaluate, analyse and compare the results obtained from the selected methods. By doing so, we aim to offer practical insights into which particular methods are better suited for specific contexts, thus aiding practitioners in selecting the appropriate approaches to address class imbalance in their machine learning tasks.
  Keywords: learning from imbalanced data; data rebalancing; ensemble learning; performance evaluation and comparison.
  DOI: 10.1504/IJBIDM.2025.10065924
   
  
• Comprehensive evaluation method of enterprise financial risk based on fuzzy grey correlation analysis  
  by Xuena Lin, Guijun Shang 
  Abstract: In this paper, a comprehensive evaluation method of enterprise financial risk based on fuzzy grey correlation analysis is proposed. Firstly, the comprehensive evaluation index system of enterprise financial risk is constructed, and the comprehensive evaluation index of risk is differentiated according to the judgment matrix. Then, based on fuzzy grey relational analysis, a judgment matrix is constructed to determine the weight of financial risk indicators. Finally, optimise the comprehensive evaluation sub-node to realise the comprehensive evaluation of enterprise financial risk. The experimental results show that the graphic area enclosed by PR curve, X-axis and Y-axis is close to 1, the financial health index is above 80%, and the false alarm rate is below 15%, which has good evaluation performance.
  Keywords: fuzzy grey correlation analysis; enterprise financial risk; comprehensive evaluation; evaluation index system; financial health index.
  DOI: 10.1504/IJBIDM.2025.10066018
   
  
• Online allocation of network learning resources based on parallel cluster mining  
  by Zhaofeng Li, Ping Hu, Pei Zhang, Liwei Zhang 
  Abstract: In order to solve the problem of low accuracy and consideration of online allocation of existing network learning resources, this paper proposes a network learning resource online allocation method based on parallel clustering mining. Firstly, analyse the development stages of online learning resources and collect data on educational resources; secondly, construct a network learning resource model and utilise parallel clustering to explore the clustering features of network learning resources; finally, using the mined resource features, design network learning resource labels to achieve online allocation of network learning resources. The experimental results show that the accuracy of network learning resource allocation in this method is 98.2%, the accuracy of network learning resource allocation is 98.1%, and the reliability of allocation reaches 96.2%.
  Keywords: parallel clustering mining; online allocation of learning resources; resource tags; education resource data.
  DOI: 10.1504/IJBIDM.2025.10066019
   
  
• Study on complement of knowledge map of educational resources based on semi-supervised learning  
  by Wei Liu 
  Abstract: In order to improve the effectiveness of completing educational resource knowledge graphs, a complement method of knowledge map of educational resources based on semi-supervised learning is studied. The relationship path features of the education resource knowledge graph are extracted using a path sorting algorithm. Within the interactive connection graph attention network of the semi-supervised deep learning algorithm, the embedding vectors of the knowledge graph are inputted to obtain the encoded representation of contextual features for the embedding vector entities, and the encoded feature matrix is constructed. The semantic matching model tensor decomposition is used to encode the feature matrix and calculate the scores for each triple. The triple with the highest score is selected as the completion result of the knowledge graph. The experimental results show that this method achieves high values in average reciprocal rank, Hits@10, Hits@3, and Hits@1, indicating a good completion effect of the knowledge graph.
  Keywords: semi-supervised learning; educational resources; knowledge map; complement method; relationship path; attention network.
  DOI: 10.1504/IJBIDM.2025.10066020
   
  
• Adaptive recommendation method for teaching resources based on knowledge graph and user similarity  
  by Meng Li 
  Abstract: To provide users with personalised and accurate teaching resource recommendation results, a new teaching resource adaptive recommendation method is proposed by effectively integrating knowledge graph with user similarity. This method first constructs a knowledge graph of teaching resources, representing the relationship between resources as a graph structure. Then, by analysing user learning history, ratings, and preferences, calculate user similarity and identify other users with higher similarity to the current user. Next, based on the resource ratings between similar users and current users, combined with the resource association relationship in the knowledge graph, the resource ratings are calculated using methods such as weighted summation. Finally, teaching resources are sorted based on resource ratings and recommended to current users. The experimental results show that the maximum root mean square error of this method is only 0.26, the highest recall rate is 95.6% and the MRR value is relatively high.
  Keywords: knowledge graph; user similarity; ICA algorithm; improve collaborative filtering algorithms; preference learning algorithm.
  DOI: 10.1504/IJBIDM.2025.10066021
   
  
• Financial risk monitoring and warning method of listed enterprises based on data mining  
  by Xinyan Zhang 
  Abstract: To address the issues of low accuracy in traditional methods for enterprise financial data mining, significant discrepancies between financial risk monitoring results and reality, and low accuracy in risk warning, a data mining-based financial risk monitoring and warning method for listed companies was designed. Firstly, grey relational clustering is used to mine financial data of listed companies. Then, factor analysis and fuzzy recognition matrix are combined to identify financial risks of listed companies. Finally, XGboost algorithm is used to divide financial risks of listed companies. Support vector machine is used to build a financial risk warning decision function for listed companies, achieving financial risk monitoring and warning for listed companies. The experimental results show that the financial risk monitoring results of this method are consistent with the true values, and the data mining accuracy can reach up to 98.23%, with a risk warning accuracy of over 95%. It has a good effect on enterprise financial risk monitoring and warning, and has high application value.
  Keywords: data mining; listed companies; financial risk; monitoring and warning; grey relational clustering; support vector machine.
  DOI: 10.1504/IJBIDM.2025.10066153
   
  
• An online ideological and political courses recommended method in colleges and universities based on weighted collaborative filtering algorithm  
  by Yun Peng, Xue Wang 
  Abstract: In order to reduce the error of course recommendation and increase the duration of online learning for students, this paper proposes a university ideological and political online course recommendation method based on weighted collaborative filtering. Firstly, the mean centred method is used to standardise user ratings; user similarity is calculated based on interest and trust. Secondly, constructing a user tag weight matrix and a course tag weight matrix helps better describe user needs. Finally, after calculating the label weights, based on the idea of collaborative filtering, the weighted generalised Mahalanobis distance is used to calculate the closeness of the recommendation scheme; the Top-N closest recommendation scheme is selected to recommend to learners. The experimental results show that this method significantly reduces course recommendation errors and improves the online learning duration of students, with a minimum recommendation error of only 0.09.
  Keywords: weighted collaborative filtering; ideological and political online courses; course recommendation; label weight.
  DOI: 10.1504/IJBIDM.2025.10066990
   
  
• A prediction method of purchasing intention of e-commerce consumers in network marketing  
  by Hongyan Da 
  Abstract: In order to improve the accuracy of prediction results, a prediction method of purchasing intention of e-commerce consumers in network marketing is proposed. Firstly, collect consumer related data, standardize and handle outliers to eliminate dimensional differences between data. Secondly, a logistic regression algorithm is used to extract and select consumer features related to purchase intention, revealing consumers’ purchasing preferences. Finally, based on the feature extraction results, a random forest algorithm is used to predict purchase intention. Gini impurity and information gain are used as splitting criteria to construct multiple decision trees, and the prediction results of all decision trees are synthesized by voting or averaging to obtain the final prediction result. The experimental results show that the root mean square error of the proposed method is relatively low, with the highest normalized information value reaching 0.89, indicating that it can accurately reflect consumers’ purchasing intention.
  Keywords: network marketing; e-commerce; purchase intention; logistic regression algorithm; random forest algorithm.
  DOI: 10.1504/IJBIDM.2025.10066993
   
  
• A feature mining model for financial product purchase behaviour data based on consumer behaviour perspective  
  by Huijun Wang 
  Abstract: To overcome the problems of low accuracy, poor precision, and poor recall caused by traditional purchasing behaviour data feature mining models, the paper proposes a financial product purchasing behaviour data feature mining model based on consumer behaviour perspective. Firstly, extract financial product purchase behaviour data, preprocess and clean it to obtain the dataset to be mined. Then, based on consumer purchasing behaviour, a consumer behaviour preference function is constructed, and the regression function is used to perform secondary processing on the analysis results. Finally, the TF-IDF algorithm is applied to extract consumer behaviour vectors, and the K-means clustering analysis method is combined to accurately mine the characteristics of purchasing behaviour data. The experimental results show that the recall rate of this method can reach 98.74%, the precision rate can reach 96.51%, and the mining accuracy rate can reach 98.65%. The mining results obtained have high reliability.
  Keywords: consumer behaviour analysis; data feature mining; TF-IDF algorithm; k-means cluster analysis.
  DOI: 10.1504/IJBIDM.2025.10066995