Depth Study of User Purchase Influencing Factors in Platform E-commerce under the Background of Big Data and AI

Abstract

Taking Taobao as a typical platform e-commerce, this study is devoted to exploring how big data and artificial intelligence technology can empower platform e-commerce and affect users' purchase intention. This paper adopts the empirical research method, collects data through online questionnaire, uses spss26 for regression analysis, and adopts the gradient lifting algorithm of machine learning model for data verification. The research results show that marketing activities such as "precise placement", "personal privacy", "product details" and "product ranking", which rely on big data and artificial intelligence technology, are the key factors affecting Taobao users' purchase intention, and their impact coefficients are 0.57, 0.135 and 0.288 respectively. Inventory management, profile building and personalized recommendations are also important factors. This paper takes the Consumer Behavior Analysis Model (AISAS) as the theoretical basis, and puts forward corresponding suggestions for Taobao platform e-commerce to enhance user attention, interest, search, purchase and sharing under the background of big data and artificial intelligence technology application.