Affiliations:
College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, China
Correspondence:
[*]
Corresponding authors. Hongjiu Liu and Yanrong Hu, College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, China. E-mail: joe_hunter@zafu.edu.cn. (H. Liu), E-mail: yanrong_hu@zafu.edu.cn. (Y. Hu)
Abstract: We studied China’s Common Prosperity process by assessing and comparing the level of Common Prosperity in different regions of China and made some beneficial recommendations to government departments. The research data comes from the China Statistical Yearbook, which includes data from 31 provinces and cities from 2015 to 2020. According to the relevant research, eleven evaluation indicators were selected. We combined GRA with the TOPSIS method for scoring and the K-means clustering algorithm for dividing the GRA-TOPSIS scoring results into three evaluation levels. Then, the convolutional neural network model was used to predict and simulate the level of common prosperity. Taking 2020 as an example, the results show: (1) From 2015 to 2020, China’s Common Prosperity level reached its highest point in 2020. Due to the impact of COVID-19 in 2019, the scores of 31 regions are generally lower than in the previous four years. The situation changed in 2020; (2) In terms of regional distribution, the economic development of Beijing, Shanghai, and other eastern regions is relatively good, with a higher degree of Common Prosperity than that of other regions; (3) The average prediction accuracy is high in our model. It can be close to 100%, indicating that the model has a good prediction effect. In addition, we made recommendations based on the research results, which have good references for actively promoting common prosperity.
Keywords: The level of China’s common prosperity, gray relational analysis method, cluster analysis, convolutional neural network
