Teaching Practice of Consumer Clicking Behavior Recognition Algorithm on E-Commerce Platform in E-Commerce Education of Higher Vocational Colleges and Universities

Abstract

With the goal of accurately mining consumer clicking behavior data on e-commerce platforms, this paper proposes a clustering algorithm based on FCM fuzzy operations, which is applied to e-commerce education in higher vocational colleges and universities. The collected e-commerce platform consumer data are normalized, and the FCM fuzzy clustering analysis algorithm is applied to the acquired two data structures, namely, dissimilarity matrix and data matrix, and after obtaining the clustering map, the data points within the space of the data structure are used as sample inputs to search for the constituent properties of the sample points and the space of the data structure, and to obtain the e-commerce platform consumer data mining results. The clustering results of the algorithm categorize the e-commerce platform consumers into three categories: selective type, comparison type and decisive type, which improves the resource allocation efficiency of the enterprise and further enhances the enterprise profit. The effect of teaching practice shows that the students in the experimental group improved their average grade by 4.3 points compared with the control group through the application of the teaching mode based on the clicking behavior recognition algorithm of consumers on the e-commerce platform. It shows that this algorithm provides a more scientific and perfect teaching mode for students of higher vocational e-commerce majors and enhances the effectiveness of teaching.