Research on the Contribution of Regional Import and Export Influential Factors Based on RBF Neural Network

Abstract

Since the reform and opening up, the proportion of imports and exports in all provinces in China has increased to a great extent, and the total import and export volume is an important condition for reflecting the economic level of a region, and it is also one of the important influencing factors affecting economic growth. In the context of economic development, by analyzing what are the factors affecting import and export, the contribution of import and export to economic growth and the reasons for promoting economic development can be analyzed. Based on this, this paper selects Zhejiang Province as the research object, uses the data from 1990 to 2020, and uses the RBF neural network algorithm to analyze this series of data. In this paper, multiple secondary indicators are selected from the five perspectives of population, industrial structure, economic development level, currency, and science and technology, and RBF training is carried out, and it is found that when the model parameter is 19, the model accuracy converges and the algorithm reaches the optimal value. According to the algorithm results, we conclude that the methods to increase the total import and export volume of the region include implementing an expansionary fiscal policy and increasing the total budget expenditure; Increase research and development funding, such as university research funding and talent subsidies; Increase industrial output value, such as providing assistance and subsidies to industrial enterprises for export orientation.