Analysis of International Trade Structure Based on Machine Learning Algorithm

Abstract

The structure of foreign trade reflects the characteristics of factor endowments of production and the efficiency of resource allocation. The level of export competitive advantage is the external manifestation of the level of economic opening up and international competitiveness, and also reflects the ability of economies to participate in international division of labor and enjoy the benefits brought by economic globalization. Industrial agglomeration is an important starting point for balancing foreign trade structure and realizing opening to the outside world in an all-round way. With the acceleration of the process of world economic integration, people are more and more deeply aware that the structure of foreign trade can better reflect the quality of a country's economic development than the scale of trade. Trade structure is not only a comprehensive reflection of a country's economic and technological level, international competitiveness of commodities and other indicators, but also one of the sources of driving force for the upgrading of its industrial structure. Based on the machine learning algorithm, this paper analyzes the impact of international trade structure on China's environment, and puts forward some countermeasures on how to reduce the negative effects of international trade on the environment and improve the positive effects of international trade on the environment under the condition of sustainable development strategy.