Trade International Competitiveness Systems Based on Artificial Intelligence

In the context of economic globalization, the economic patterns of various countries are constantly adjusting, and the focus of international trade has shifted from traditional commodity trade to service trade. Service trade has become an important component of global economic development, and Chinese service trade has also developed rapidly in recent years. Therefore, it is necessary to study the international competitiveness of service trade, analyze the root causes of its lagging development, and explore strategies to enhance its competitiveness. This article aimed to explore a trade international competitiveness analysis system based on Principal Component Analysis (PCA) and Support Vector Machines (SVM) algorithms. Firstly, this article analyzed and extracted relevant factors of international trade competitiveness from the perspective of factor analysis. Secondly, this article introduced the application of support vector machine algorithm in the analysis of international trade competitiveness, and explored its advantages in pattern recognition and prediction. The experiment showed that the results calculated by the algorithm model were very close to the actual export value, with a relative error of almost 0%, which indicated that the accuracy of the algorithm model in this paper was extremely high.