Enterprise Trade Economic Forecast Model Based on Genetic Optimization Graph Neural Networks
The economic benefits of foreign trade enterprises are influenced by multiple related factors such as product type,policy and tax rate,so it is necessary to make a scientific analysis of the future foreign trade economic forecast. On the basis of intelligent algorithm research,a genetic optimization graph neural network (GOGNN) model is designed to realize the function of enterprise trade economic prediction. Ac-cording to the characteristic factors of economy and trade,the crossover probability and mutation probability are dynamically adjusted by introducing the average value of population fitness and the dispersion degree of fitness value. According to the evolution of the population,these probabilities are dynamically adjusted to avoid the model falling into the local optimal solution. The evaluation indexes that affect the trade economy are selected to construct the economic and trade forecast graph data,the graph neural network is used to ag-gregate the index factors,and the forecast accuracy is taken as the output value of the fitness function. Com-bined with the historical foreign trade economic data from the National Bureau of Statistics,the State Ad-ministration of Fo-reign Exchange,industry research and other websites,the proposed GOGNN is compared with the other models. The experimental results show that the mean square error of GOGNN to the training data is about 0.74335,which is smaller than the other models. The prediction error of GOGNN model for test data set is below 0.03,and it has better convergence accuracy than other models.
万方数据
维普
