The application of consumer behavior prediction based on deep learning model in cross-border e-commerce

With the rapid development of digital technology, cross-border e-commerce has become an important part of international trade. However, in the rapidly changing market environment, accurate prediction of consumer behavior has become a major challenge for cross-border e-commerce enterprises. Traditional prediction models have limitations in handling complex and variable consumer behavior data, and therefore new approaches need to be explored to improve prediction accuracy. This paper investigates the application of deep learning models in consumer behavior prediction. Analyze the new changes and influencing factors of cross-border e-commerce consumer behavior, and constructs and compares the deep learning models such as DNN, rDNN and KmDNN. Experimental results show that processing unbalanced data can improve the prediction performance of the model, but not the rDNN model. Compared with traditional prediction models, deep learning models have enormous potential. In the practical application of e-commerce platforms, deep learning models have achieved significant results in sales forecasting, user profiling and precision marketing, intelligent customer service and after-sales support, effectively improving operational efficiency and reducing costs.