This paper presents a research on e-commerce logistics and supply chain network optimization for cross-border trade. The study focuses on developing a short-term demand-based deep neural network and cold supply chain optimization method to predict commodity purchase volume. The deep neural network technique proposes a cold supply chain demand forecasting framework centered on multilayer Bayesian networks (BNN) to forecast the short-term demand for e-commerce goods. The cold supply chain (CS) optimization method determines the optimized management inventory. The research findings demonstrate that this study considers various influencing factors and chooses an appropriate forecasting technique. The proposed method outperforms 96.35% of Accuracy, 97% of Precision and 94.89% of Recall. Cross-border e-commerce has started to quickly take over the retail sector as individuals' standards of living have been rising, and electronic networks have become more and more integrated into daily life. The advent of global e-commerce has spurred the development of the logistics business. As a result, a hot problem has emerged regarding the consistent supply of cold chain commodities in cross-border e-commerce. Large-scale buying and allocation, unbalanced order quantities, and large-scale price changes are potential contributors to supply chain imbalances. The entire e-commerce system will collapse if such a thing happens. Studying cross-border e-commerce chain supply inventory control is crucial to ensuring that goods can move swiftly and consistently through an entire supply chain and react quickly whenever the chain is out of balance. The term 'supply chain' refers to the network of people, organisations, resources, activities, and technologies involved in producing and selling a product. The process starts from raw material extraction to final product delivery to the consumer. A cold supply chain, on the other hand, is a subset of the supply chain that specialises in preserving, storing, and transporting temperature-sensitive products. Food, pharmaceuticals, and certain chemicals are perishable goods that require a controlled temperature environment to maintain quality and prevent spoilage. The main motivation behind this research is to process the sensitive food transformation from manufacturing to retail stores with utmost care. There is a pressing need for advanced demand forecasting and efficient supply chain management in the rapidly evolving e-commerce landscape, particularly in cross-border transactions. The proposed research addresses this by creating a Deep Neural Network based on Short-Term Demand and a Cold Supply Chain Optimization method. This approach is especially pertinent given the complexities of consumer demand patterns and the logistical challenges inherent in managing a global supply chain. The research aims to improve the accuracy of commodity sales forecasts by leveraging sophisticated deep-learning techniques, allowing for more effective procurement and inventory optimisation. This is critical for retaining competitiveness in a fast-paced market and ensuring sustainability and responsiveness in the face of changing market demands and environmental concerns.This paper presents a research on e-commerce logistics and supply chain network optimization for cross-border trade. The study focuses on developing a short-term demand-based deep neural network and cold supply chain optimization method to predict commodity purchase volume. The deep neural network technique proposes a cold supply chain demand forecasting framework centered on multilayer Bayesian networks (BNN) to forecast the short-term demand for e-commerce goods. The cold supply chain (CS) optimization method determines the optimized management inventory. The research findings demonstrate that this study considers various influencing factors and chooses an appropriate forecasting technique. The proposed method outperforms 96.35% of Accuracy, 97% of Precision and 94.89% of Recall. Cross-border e-commerce has started to quickly take over the retail sector as individuals' standards of living have been rising, and electronic networks have become more and more integrated into daily life. The advent of global e-commerce has spurred the development of the logistics business. As a result, a hot problem has emerged regarding the consistent supply of cold chain commodities in cross-border e-commerce. Large-scale buying and allocation, unbalanced order quantities, and large-scale price changes are potential contributors to supply chain imbalances. The entire e-commerce system will collapse if such a thing happens. Studying cross-border e-commerce chain supply inventory control is crucial to ensuring that goods can move swiftly and consistently through an entire supply chain and react quickly whenever the chain is out of balance. The term 'supply chain' refers to the network of people, organisations, resources, activities, and technologies involved in producing and selling a product. The process starts from raw material extraction to final product delivery to the consumer. A cold supply chain, on the other hand, is a subset of the supply chain that specialises in preserving, storing, and transporting temperature-sensitive products. Food, pharmaceuticals, and certain chemicals are perishable goods that require a controlled temperature environment to maintain quality and prevent spoilage. The main motivation behind this research is to process the sensitive food transformation from manufacturing to retail stores with utmost care. There is a pressing need for advanced demand forecasting and efficient supply chain management in the rapidly evolving e-commerce landscape, particularly in cross-border transactions. The proposed research addresses this by creating a Deep Neural Network based on Short-Term Demand and a Cold Supply Chain Optimization method. This approach is especially pertinent given the complexities of consumer demand patterns and the logistical challenges inherent in managing a global supply chain. The research aims to improve the accuracy of commodity sales forecasts by leveraging sophisticated deep-learning techniques, allowing for more effective procurement and inventory optimisation. This is critical for retaining competitiveness in a fast-paced market and ensuring sustainability and responsiveness in the face of changing market demands and environmental concerns.