Cross-border e-commerce customer behavior prediction model and business decision optimization based on deep learning

With the rapid development of cross-border e-commerce, how to predict customer behavior is of great research significance . This paper proposes a customer behavior prediction model based on deep learning, which mainly constructs a two -layer neural network prediction framework by analyzing the customer's purchase history data and behavior characteristics . The model integrates the long short-term memory network (LSTM) and the XGBoost algorithm, which can effectively capture the potential rules in time series data, so that customer behavior can be predicted to a certain extent . Finally, an empirical study was conducted on the data of a cross-border e-commerce platform, which verified that the model is feasible within a certain range.