Title: Detection method of e-commerce cluster consumption behaviour based on data feature mining

Authors: Ming Yang

Addresses: School of Business, Chongqing College of Electronic Engineering, Chongqing 401331, China

Abstract: In order to effectively improve the accuracy and efficiency of e-commerce cluster consumption behaviour detection, an e-commerce cluster consumption behaviour detection method based on data feature mining is proposed. The concept and process of data feature mining and the e-commerce cluster consumption behaviour are analysed, and the characteristics of the e-commerce cluster consumption behaviour data with multiple characteristics are extracted. The Laplace feature mapping method is used to pre-process the extracted data features of e-commerce cluster consumption behaviour, the cyclic neural network structure is used to classify the data of e-commerce cluster consumption behaviour, and the data feature mining method is used to construct the detection model of e-commerce cluster consumption behaviour, so as to realise the detection of e-commerce cluster consumption behaviour. Experimental results show that the proposed method can effectively improve the detection accuracy and efficiency of e-commerce cluster consumption behaviour.

Keywords: data feature mining; cyclic neural network; Laplace feature mapping; e-commerce clustering; consumer behaviour detection.

DOI: 10.1504/IJRIS.2023.128378

International Journal of Reasoning-based Intelligent Systems, 2023 Vol.15 No.1, pp.29 - 34

Received: 18 Feb 2022
Accepted: 28 Apr 2022
Published online: 18 Jan 2023 *