Research on cross-border e-commerce sales forecasting based on deep learning

Accurately predicting sales volume in order to formulate reasonable procurement, logistics and marketing plans is of great significance for cross-border e-commerce companies to meet consumer demand, reduce operating costs and improve profitability. Aiming at the problem of sales volume prediction of cross-border e-commerce, this study constructs a cross-border e-commerce sales volume prediction model based on deep learning based on the theory of rational negligence. According to the differences in the degree of perception in the consumer decision-making process, the explanatory power of variables is deeply analyzed to identify effective prediction features. The prediction performance of the two models LSTM/Seq2Seq is combined to establish a more accurate sales volume prediction model. The experimental results show that the model in this study combines the advantages of both LSTM/Seq2Seq, so that it can effectively capture the inherent laws between data and can effectively conform to the long-term change relationship of data. Compared with traditional models, it has better prediction performance.