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Risk Prediction of the Development of the Digital Economy Industry Based on a Machine Learning Model in the Context of Rural Revitalization

Author

Listed:
  • Rui Luan

    (Shenyang Polytechnic College, China)

  • Ping Xu

    (Xihua University, China)

Abstract

In today's society, rural areas face challenges such as complex terrain and uneven population distribution, and infrastructure construction is exceptionally difficult. At the same time, poor information transmission and low communication efficiency have also become a major obstacle to the promotion of the digital economy in rural areas. This study aims to use gradient advancement models to identify potential risks in the growth of the digital economy sector related to rural revitalization. In this study, we used an enhanced hierarchical gradient boosting algorithm. The research results indicate that the introduction of this technology can provide us with a more comprehensive and reliable risk prediction model, thereby more scientifically assisting the development and decision-making of the digital economy in rural areas. This article provides a new perspective and solutions for development issues in rural areas, promoting sustainable development and economic growth in rural areas.

Suggested Citation

  • Rui Luan & Ping Xu, 2024. "Risk Prediction of the Development of the Digital Economy Industry Based on a Machine Learning Model in the Context of Rural Revitalization," Information Resources Management Journal (IRMJ), IGI Global Scientific Publishing, vol. 37(1), pages 1-21, January.
    • Handle: RePEc:igg:rmj000:v:37:y:2024:i:1:p:1-21
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    References listed on IDEAS

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    1. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Using Survey Data to Forecast Real Activity with Evolutionary Algorithms. a Cross-Country Analysis," Journal of Applied Economics, Taylor & Francis Journals, vol. 20(2), pages 329-349, November.
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    3. Chengkai Zhang & Yanjun Zhang & Yu Li & Shan Li, 2023. "Coupling Coordination between Fintech and Digital Villages: Mechanism, Spatiotemporal Evolution and Driving Factors—An Empirical Study Based on China," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    4. Siddhartha Vadlamudi, 2020. "The Impacts of Machine Learning in Financial Crisis Prediction," Asian Business Review, Asian Business Consortium, vol. 10(3), pages 171-176.
    5. Petropoulos, Anastasios & Siakoulis, Vasilis & Stavroulakis, Evangelos & Vlachogiannakis, Nikolaos E., 2020. "Predicting bank insolvencies using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1092-1113.
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