Analysis of influencing factors of regional import and export trade based on an artificial neural network

Abstract

In the post-epidemic era, the global political and economic pattern has accelerated the evolution, and regional imports and export tr[ade has become an important way of political and economic communication between countries. As the research area, this paper selects Shanghai, China, establishes a comprehensive evaluation index system of influencing factors of regional import and export trade, and constructs a high-precision artificial neural network (ANN) model to quantitatively evaluate import factors and export trade in Shanghai, China. The results show that freight capacity, science, technology level, and exchange rate change have a greater impact on the import and export trade in the region. Improving the level of transportation, science, and technology in Shanghai is significant to the sustainable development of Shanghai's foreign trade. The Chinese government can appropriately expand the geographical distribution of import and export business, establish a particular currency portfolio, and disperse foreign exchange risks in international trade and investment, and financing. The Chinese government can consider providing cash flow support for enterprises, which promotes consumption investment and vigorously improves the VAT retention and rebate system. It can focus on solving the problem of tax retention and rebate in scientific research, technical services, and transportation industries to improve market confidence and promote the development of China's import and export trade.