Statistical Analysis of Guangxi's Per Capita GDP Based on Deep Neural Network Model

Abstract:
This article analyzes Guangxi's per capita GDP-related data based on deep neural networks. The data set mainly includes 5 secondary indicators of tourism, ecology, investment, education and consumption levels, and 10 three-level indicators include: total number of inbound tourists, industrial wastewater Emissions and other aspects are introduced in detail. After verification, it is concluded that the constructed model has good training and test results, and it shows that there is a non-linear relationship among various indicators. The deep learning method proposed in this paper has a good reference effect for the training and testing of the per capita GDP nonlinear simulation system, provides suggestions for Guangxi's future economic development planning, and provides a reference basis for optimizing the fiscal structure.