Research Article
Construction of High-quality Economic Development Indicator System Based on Unsupervised Learning
@INPROCEEDINGS{10.4108/eai.12-1-2024.2347186, author={Liwen Tang and Liuyang Bian and Zhiang Ma and Guangxia Zhao and Qi Wang}, title={Construction of High-quality Economic Development Indicator System Based on Unsupervised Learning}, proceedings={Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12--14, 2024, Ningbo, China}, publisher={EAI}, proceedings_a={BDEDM}, year={2024}, month={6}, keywords={high-quality economic development; unsupervised learning; spectral clustering; laplacian}, doi={10.4108/eai.12-1-2024.2347186} }- Liwen Tang
Liuyang Bian
Zhiang Ma
Guangxia Zhao
Qi Wang
Year: 2024
Construction of High-quality Economic Development Indicator System Based on Unsupervised Learning
BDEDM
EAI
DOI: 10.4108/eai.12-1-2024.2347186
Abstract
This research mainly studies the problems currently encountered in the construction of the indicator system for high-quality economic development in China. The indicator system for high-quality economic development in China is not sound enough. Most of the research is based on the relevant concepts of the new development stage and the report of the 19th National Congress of the Communist Party of China. To construct an evaluation indicator system, there is a lack of quantitative method. This paper proposes a method of quantitatively constructing an indicator system based on the indicator systems constructed by other scholars, using spectral clustering combined with the unsupervised learning method of Laplacian score. The results were tested and preliminary research results were obtained. This method can be used to conduct deeper analysis and obtain more instructive conclusions.