Research on Cross-border Electronic Commerce Inventory Prediction Model according to C-A-XGBoost Algorithm

In the era of big data, cross-border electronic commerce enterprises have begun to employ data mining algorithms to extract valuable business information from massive data to enhance their competitiveness in overseas markets. With the consumption upgrading, cross-border electronic commerce users have propose higher requirements for enterprises' service levels. As a key factor influencing inventory management, inventory prediction significantly impacts improving customer service levels. To improve the accuracy of inventory prediction, according to current cross-border electronic commerce inventory, this paper proposes a cross-border electronic commerce inventory prediction method according to demand characteristics and sequence trends, namely the C-A-XGBoost model. The prediction model proposed of this paper contributes to a scientific reference for cross-border electronic commerce inventory prediction, which helps cross-border electronic commerce enterprises gain precise inventory preparation, improves user satisfaction, and enhances their competitiveness in overseas markets.