Against the backdrop of deepening globalization, risks and uncertainties in the international economic and trade system are increasing, and there is an urgent need to establish an efficient early warning system to predict and assess potential economic crises. Therefore, this paper applies PageRank algorithm to the international economic and trade early warning system to improve the accuracy and reliability of the early warning system. First, this paper analyzes the economic relations and trade data of various countries in international economic and trade, and constructs a national economic and trade relationship network. Finally, this paper uses the core idea of the PageRank algorithm to evaluate the impact of various countries’ economies on the global economic system by calculating the “importance” value of the node, and constructs a risk assessment model based on this. Verified by experimental data, the results show that the PageRank algorithm can effectively identify key economies and high-risk countries in the global trade network. For example, countries with higher economic dependence, such as the United States (20% dependence, PageRank value 0.25), show higher risk values, while countries with lower economic dependence, such as Canada, have lower risk values. These values verify the feasibility and effectiveness of the PageRank algorithm in the international economic and trade early warning system, significantly improve the prediction accuracy and reliability of the early warning system, and provide policymakers with a powerful risk assessment tool.