Analysis of regional economic evaluation based on machine learning

被引:2
作者
Xu, Xiaoying [1 ]
Zeng, Zhijian [2 ]
机构
[1] South Cent Univ Nationalities, Sch Econ, Wuhan, Peoples R China
[2] Hunan Univ, Business Sch, Changsha, Hunan, Peoples R China
关键词
Machine learning; regional economy; simulation model; economic evaluation; BIG DATA; PREDICTION; ANALYTICS;
D O I
10.3233/JIFS-189575
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The regional economic evaluation and analysis has guiding significance for the subsequent economic strategy formulation. Due to the influence of various factors, the volatility of some current economic evaluation models is relatively large. According to the needs of regional economic evaluation, this study uses computer technology combined with regional economic development to build an economic development evaluation model to evaluate and analyze the regional economy. Through comparative analysis, this study selects the entropy weight-TOPSIS model as the comprehensive evaluation model of regional economy, uses the entropy weight method to determine the weight of each index, and then uses the TOPSIS method to conduct comprehensive evaluation. In addition, this study designs a control experiment to analyze the performance of this study model. Moreover, this study uses the model proposed in this study to conduct regional economic evaluation in recent years, and compares it with real data, and observes the test results with statistical charts and table data. The research results show that this research model has a certain effect, which can provide analytical tools for the follow-up economic strategy research and analysis.
引用
收藏
页码:7543 / 7553
页数:11
相关论文
共 50 条
  • [11] Regional level influenza study based on Twitter and machine learning method
    Xue, Hongxin
    Bai, Yanping
    Hu, Hongping
    Liang, Haijian
    [J]. PLOS ONE, 2019, 14 (04):
  • [12] RETRACTED: A New Machine Learning Algorithm for Regional Low-Carbon Economic Development Analysis Based on Data Mining (Retracted Article)
    Liu, Xinlei
    [J]. JOURNAL OF FUNCTION SPACES, 2022, 2022
  • [13] The Challenges of Machine Learning and Their Economic Implications
    Borrellas, Pol
    Unceta, Irene
    [J]. ENTROPY, 2021, 23 (03) : 1 - 23
  • [14] Internet financial supervision based on machine learning and improved neural network
    Cao Yanli
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 40 (04) : 7297 - 7308
  • [15] Consumer behavior analysis model based on machine learning
    Li, Zhou
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 40 (04) : 6433 - 6443
  • [16] Analysis of Growth Performance in Swine Based on Machine Learning
    Lee, Woongsup
    Ham, Younghwa
    Ban, Tae-Won
    Jo, Ohyun
    [J]. IEEE ACCESS, 2019, 7 : 161716 - 161724
  • [17] A review of psoriasis image analysis based on machine learning
    Li, Huihui
    Chen, Guangjie
    Zhang, Li
    Xu, Chunlin
    Wen, Ju
    [J]. FRONTIERS IN MEDICINE, 2024, 11
  • [18] Machine learning-based correlation for economic evaluation of HTSE-nuclear cogeneration plant
    Sadeghi, Khashayar
    Ghazaie, Seyed Hadi
    Sokolova, Ekaterina
    Sergeev, Vitaly
    Ksenia, Naypak
    Yang, Luopeng
    [J]. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2025, 114 : 337 - 351
  • [19] Regional economic integration and machine learning: Policy insights from the review of literature
    De Lombaerde, Philippe
    Naeher, Dominik
    Vo, Hung Trung
    Saber, Takfarinas
    [J]. JOURNAL OF POLICY MODELING, 2023, 45 (05) : 1077 - 1097
  • [20] Evaluation of agricultural climate and regional agricultural economic efficiency based on remote sensing analysis
    Lu X.
    [J]. Arabian Journal of Geosciences, 2021, 14 (10)