This paper analyzes the cross-border e-commerce industry chain, and explores its customer value and impact on marketing strategy through data mining techniques.The whole process data mining methodology of data cleaning, integration, selection, transformation, mining, pattern evaluation and knowledge representation is adopted to deal with the cross-border e-commerce data.This paper focuses on the analysis of the customer value, the construction of the user profile, and the user behavioral patterns based on RFM model.The results show that the customer value is mainly determined by the customer life cycle and the purchase rate. The results show that the customer life cycle and purchase rate mainly determines the customer value of cross-border e-commerce.In the construction of user profiles, the characteristics and behavioral patterns of different users can be effectively identified through data standardization and the construction of user attribute models.The RFM model reveals the different levels of the user’s activity level, consumption ability and consumption value, which provides the basis for formulating differentiated marketing strategies.The data show that among the 4,130 user samples, the user behaviors and behavioral patterns were identified through clustering and knowledge representation. In the sample of 4130 users, cluster analysis shows that most of the users (72.64%) are ordinary customers. In contrast, the proportion of high-value customers (VIP customers) is only 0.24%, which suggests that cross-border e-commerce platforms need to formulate corresponding service and marketing strategies for different user groups to improve customer satisfaction and purchase conversion rate. At the same time, the cross-border e-commerce industry is experiencing changes such as platform fission, marketing innovation, and sinking of service targets, which poses new challenges and opportunities for e-commerce platforms’ operation and management.