Design and Simulation of International Trade Risk Control System for Digital Economy Based on Machine Learning Algorithm

In the context of an increasingly interconnected global economy, international trade faces a myriad of risks that can significantly impact businesses and economies. The advent of the digital economy has further complicated these challenges, necessitating innovative approaches to risk management. This paper presents the design and implementation of an International Trade Risk Control System (ITRCS) that leverages machine learning algorithms to identify, assess, and mitigate risks associated with international trade transactions.The proposed system integrates various data sources, including trade patterns, economic indicators, and geopolitical developments, to develop a comprehensive risk assessment framework. Machine learning models, such as decision trees, support vector machines, and neural networks, are employed to analyze historical data and predict potential risks, enabling proactive decision-making.The ITRCS is designed to provide real-time insights and alerts to stakeholders, facilitating timely responses to emerging threats. Through case studies and simulations, the effectiveness of the system is demonstrated, highlighting significant improvements in risk detection and management efficiency compared to traditional methods.This research contributes to the field of international trade by offering a robust, data-driven tool that enhances risk control mechanisms, ultimately promoting safer and more resilient trade practices in the digital economy. The findings underscore the potential of machine learning as a transformative force in risk management, paving the way for future advancements in the domain.