Cross-Border E-Commerce User Behavior Analysis Algorithm Based on Deep Learning

In the field of cross-border e-commerce, enterprises need to understand and predict user behavior, because this is crucial, and it can affect the accuracy of product recommendations and the effectiveness of marketing strategies. Based on the deep learning algorithm and RNN model, this paper explores its application value in analyzing and predicting cross-border e-commerce user behavior. As a deep learning algorithm, RNN can effectively process user behavior data due to its powerful performance in sequential data processing. Based on this, this paper uses theoretical analysis, qualitative analysis, quantitative analysis, and empirical analysis methods to comprehensively evaluate the different performance of RNNs in different types of user behavior prediction, and compare them with other common models. The results show that RNN is a superior deep learning algorithm, based on which the system can effectively capture the time dependence of user behavior and greatly improve the prediction accuracy and efficiency. Finally, this paper also focuses on the analysis of the specific application strategies and subsequent challenges of deep learning in actual cross-border e-commerce platforms, so as to provide sufficient application guidance and suggestions for e-commerce enterprises.