The feature classification method of mobile e-commerce big data under the webcast mode Online publication date: Wed, 10-Jan-2024
by Jie Li
International Journal of Networking and Virtual Organisations (IJNVO), Vol. 29, No. 3/4, 2023
Abstract: In order to improve the special effect of classification results and reduce the convergence classification tolerance, a feature classification method of mobile e-commerce big data under the webcast mode was designed in this paper. Firstly, the dynamic mining of mobile e-commerce big data is realised by setting sliding windows and according to the association mining rules between data. Then, the data weight is calculated based on the TF-IDF method, and the data features are extracted through normalisation processing. Finally, after the data features are derived, the multi-label merging is implemented, and then the feature classification is realised by traversing the feature values using the fuzzy mathematics theory. Experimental results show that the classification specificity interval of this method is [93.1%, 95.6%], and the convergence classification tolerance interval is [0.029, 0.055], indicating that the reliability of this method is high.
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