Design and Simulation of International Trade Pathway for Digital Economy Based on Machine Learning Algorithm

In the rapidly evolving landscape of the digital economy, understanding and optimizing international trade pathways is crucial for enhancing economic efficiency and competitiveness. This study presents a comprehensive design and simulation framework utilizing advanced machine learning algorithms to analyze and predict trade patterns in the digital economy. By leveraging large datasets encompassing trade volumes, digital goods, economic indicators, and policy variables, In this paper develop predictive models that identify key factors influencing international trade dynamics. Our approach employs techniques such as supervised learning for demand forecasting and unsupervised learning for clustering trade relationships. The simulation component allows for scenario analysis, enabling stakeholders to visualize potential trade outcomes under varying conditions and policy interventions. This research aims to provide actionable insights for policymakers, businesses, and researchers, facilitating informed decision-making and strategic planning in the context of global digital trade. Ultimately, our findings contribute to a deeper understanding of the interplay bet. In this paper en digital transformation and international trade, highlighting the need for adaptive strategies in a digitally driven global marketplace.