Application of Deep Learning Technology in Global Electronic Information Management and Evaluation Under the Perspective of International Trade Law

Abstract

Global Electronic Information Management (GEIM) plays a crucial role in streamlining data processing, enhancing security, and ensuring regulatory compliance in international trade. With the increasing volume of cross-border transactions, efficient information management systems are essential to handle trade documentation, legal compliance, fraud detection, and secure communication between trade entities. This paper presents HCEIM-DL (Hidden Chain Ethereum Information Management with Deep Learning) as an advanced framework for electronic information management and international trade law enforcement. By integrating deep learning, blockchain, and AI-driven compliance systems, HCEIM-DL significantly enhances trade security, fraud detection, compliance accuracy, and processing efficiency. The model achieves a trade compliance accuracy of 96.8%, fraud detection rate of 95.4%, and legal contract verification accuracy of 99.3%, ensuring robust regulatory adherence. With a transaction processing speed of 7,200 TPS, HCEIM-DL outperforms traditional systems by 85.7%, enabling faster and more efficient trade operations. The model also improves data transparency to 99.2%, reducing the risk of legal disputes, and cuts compliance costs by 78.6%, making global trade more affordable. Additionally, customs clearance efficiency increases to 95.8%, reducing trade delays, while dispute resolution time decreases by 66.7%, from 45 days to just 10 days. Energy consumption per transaction is optimized with a 37.8% reduction, ensuring a sustainable and scalable system. These results highlight HCEIM-DL as a transformative approach to trade law enforcement, enhancing security, efficiency, and compliance while reducing costs and risks in international trade.