Product development and innovation is the key issue for cross-border e-commerce operation. It is of great importance to build an intelligent open innovation system and maximize its proximity to market demand with the internationalization and digital advantages of cross-border e-commerce. Cross border e-commerce data is widely distributed among each node enterprise in the supply chain. But the enterprises will not share private data for intelligent learning for the sake of data security, which has become a difficulty problem in intelligent decision-making of open innovation. This paper analyzes the research status of open innovation and the technical basis of federated learning, builds an open innovation intelligent decision-making model of cross-border e-commerce based on federated learning, trains and tests the model using data from two participants, compares the intelligent prediction effects among whole learning, local learning and federated learning, and puts forward the collaborative promotion strategy of open innovation and intelligent optimization of cross-border e-commerce based on federated learning.

Cross-border e-commerce has become an important way of “New Infrastructure for Foreign Trade” and “Online Silk Road construction.” Utilizing intelligent technology to energize cross-border e-commerce can improve the quality and efficiency of the whole industrial chain. Cross-border e-commerce enterprises generally face the problem of product customization and development due to the large difference in international market demand. This study first analyzes the research status and technical underpinnings of intelligent customization of cross-border e-commerce, then establishes the technical framework of intelligent customization of cross-border e-commerce based on in-depth learning, and subsequently trains, tests, and analyzes the model, and the intelligent customization model has achieved a higher learning level. Finally, it puts forward the promotion and application strategy of cross-border e-commerce intelligent customization based on deep learning. It has theoretical significance and practical value for intelligently identifying changes in international market demand, helping cross-border e-commerce enterprises select products, and optimizing cross-border e-commerce product development.

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