Data of E-Commerce Users Based on Data Mining Technology

With the rapid development of information technology, the rapid development of e-commerce Internet has been involved in every corner, resulting in the growth of the data volume of e-commerce users. This paper mainly studies the data analysis of e-commerce users based on data mining technology. This paper introduces the research background and significance of e-commerce user behavior. On this basis, relevant technologies of e-commerce user behavior analysis are studied, including components of Spark platform and classification algorithm of relevant user behavior analysis model. Then, the advantages and feasibility of the improved model are verified through comparative experiments between the improved Spark XGBoost model and traditional machine learning method. The study in this paper provides a parallel method for the prediction of e-commerce user behavior, which can be applied to daily life as an effective method to predict e-commerce behavior.