Research on China’s Primary Industry: Evidence From Regional Analysis Based on SVM and Moran’s Index

Abstract

With advanced technology and efficient policy management in China’s primary industry, productivity has increased significantly. This article aims to use machine learning and Moran’s I to analyze the current situation of China’s primary industry from a regional perspective. Principal component analysis and Lagrange polynomial interpolation are used for data pre-processing. Classification result from the support vector machine reveals that there exist boundaries between each region based on the features of the primary industry. Our results show that fishery and forestry show positive spatial correlations in the Moran’s I scatter diagram, while animal husbandry and farming show negative spatial correlations, and regional agriculture development can improve China’s primary industry in the long run.