User Behavior Data Mining and Analysis of E-Commerce Platforms: a Study Based on Big Data

The purpose of this study is to use big data technology to deeply dig and analyze the user behavior of e-commerce platforms, so as to solve the problems existing in traditional research, such as limited sample size, single data source, limited analysis methods, insufficient timeliness, and inability to deal with unstructured data. Based on the user behavior data of e-commerce platform, this study adopts K-Means++, K-means and hierarchical clustering algorithm to perform cluster analysis, focusing on recall rate as an evaluation index. Experimental results show that K-Means++ performs well in the recall rate, reaching 83%, which has a significant advantage over K-Means (63%) and hierarchical clustering (45%). The study highlights the excellent performance of K-Means++ in the analysis of e-commerce user data, providing a reliable way to improve personalized recommendations and user experience. The research results have guiding significance for e-commerce platform data mining and user behavior analysis, and provide strong support for practical application.