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In this paper, the adaptive lasso method is used to screen variables, and different neural network models of seven countries are established by choosing variables. Gross domestic product (GDP) is a function of land area in the country, cultivated land, population, enrollment rate, total capital formation, exports of goods and services, and the general government’s final consumption of collateral and broad money. Based on the empirical analysis of the above factors from 1973 to 2016, the results show that the BP neural network model has better performance based on multiple summary statistics, without increasing the number of parameters and better predicting short-term GDP. In addition, the change and the error of the model are small and have a certain reference value.

BP Neural Network Calculus in Economic Growth Modelling of the Group of Seven