User Behavior Analysis and Prediction Algorithm Design of E-commerce Platform Based on Big Data Analysis

From the consumer’s point of view, e-commerce is online shopping, which is deeply loved by consumers because of its convenient price comparison, unlimited commodity selection, rapidity and convenience. Faced with a large number of EC (Electronic Commerce) website shopping users, it is particularly difficult to analyze users’ behaviors. Therefore, this paper designs the user behavior analysis and prediction algorithm of e-commerce platform based on big data analysis, and through experimental analysis, it can be found that the types of books purchased by students in traditional bookstores are also different from those purchased online. Students often buy exam guidance books in traditional bookstores, accounting for an average of 47.51% of all books; The most frequently purchased books in online bookstores are exam counseling, accounting for an average of 82.61% of all books. This shows that the algorithm designed in this paper can provide marketing decisions for EC websites, reduce marketing costs, guard against operational risks, expand the scale of EC users and the successful shopping rate, enhance EC benefits and enhance market competitiveness.