Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12–14, 2024, Ningbo, China

Research Article

Construction of High-quality Economic Development Indicator System Based on Unsupervised Learning

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  • @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
Liwen Tang1, Liuyang Bian1, Zhiang Ma1, Guangxia Zhao1, Qi Wang2,*
  • 1: Anhui University
  • 2: Chinese Academy of Sciences
*Contact email: wangqi@ipp.ac.cn

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.