Research and Application of User Behavior Data Analysis Technology for E-commerce

Abstract With the rapid development of the Internet, e-commerce has become an important engine of national economic growth. User behavior data analysis is of great value in the field of e-commerce. It can help enterprises better understand consumer needs, optimize marketing strategies, and improve user experience. In this study, the data analysis technology of e-commerce user behavior has been deeply studied, and remarkable results have been achieved in practical application. E-commerce platforms have accumulated a lot of user data, and how to mine valuable information from this massive data has become a hot research topic at present. Based on a large e-commerce platform, this study uses data mining and machine learning technologies to analyze user behavior data, aiming to provide accurate market positioning and personalized recommendations for enterprises. During the experiment, 500 million pieces of user behavior data were first preprocessed, and the effective data accounted for 90%. On this basis, using the association rule mining algorithm, more than 10 user behavior patterns are found, such as “browse-collect-buy” and so on. Then, a user behavior prediction model based on deep learning is constructed. The accuracy rate of the model in predicting user purchase intention reaches 85%, which is 15% higher than that of the traditional model. This study also applies the analysis results to the personalized recommendation system of an e-commerce platform. Through comparative experiments, we find that the recommendation system with this research method has increased the user click-through rate by 20% and the conversion rate by 15%.