Research on nonlinear forecast and influencing factors of foreign trade export based on support vector neural network

As a kind of time series, the export volume of foreign trade has the characteristics of randomness, complexity, strong nonlinearity and noise, so it is difficult to describe it by using the traditional time series model algorithm. Support vector neural network (SVNN) has many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition problems. This paper uses the method of support vector neural network to predict and analyze China's foreign trade export and uses principal component analysis and regression analysis to analyze the contribution rate of different influencing factors to foreign trade export. The results show that:(1) the contribution rate of domestic economic factors to China's foreign trade export is the largest, reaching 59.65%, which also reflects the necessity and correctness of China's insistence on supply-side reform. (2) The nonlinear prediction results of the support vector neural network have a good fitting with the actual value of China's foreign trade export, and the prediction error of the support vector neural network is controlled within 10%, showing a good prediction effect; (3) neural network method has good modeling and generalization ability for nonstationary small sample import and export time series data and can achieve high prediction accuracy and decision judgment accuracy, especially for the prediction of its development trend, and the model has a high degree of fitting.