Cross-Border E-Commerce Platform Logistics and Supply Chain Network Optimization Based on Deep Learning

Abstract

E-commerce and logistics are symbioses with each other, but cross-border e-commerce (CBEC) still cannot break away from cross-border logistics. With the progress of economic internationalization, economic and trade ties around the world have become closer and closer, and the level of international business exchanges has been improved. The rise of multinational e-commerce has also caused unprecedented difficulties to multinational logistics and supply chain management. The application of deep neural networks in various fields provides opportunities for cross-border e-commerce platforms to solve these problems. The existing logistics distribution model cannot keep up with the development of CBEC and has become a constraint and bottleneck for the development of CBEC. Therefore, this article introduces deep learning neural network to cross-border logistics and supply chain based on the analysis of the existing cross-border logistics model and supply chain model and the status quo of e-commerce development. It optimizes the existing cross-border logistics and supply chain network in order to break through the current bottleneck in the development of CBEC. This paper shows through research that introducing deep learning neural networks into CBEC logistics and supply chain can improve the efficiency of logistics and supply chain. Compared with the previous efficiency, the efficiency of network optimization can be increased to about 50%, reducing the cost of cross-border logistics and supply chain. The research in this article has great theoretical and guiding significance for the development of CBEC.