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The Sustainable Rural Industrial Development under Entrepreneurship and Deep Learning from Digital Empowerment

Author

Listed:
  • Suwei Gao

    (Research Institute of China’s Rural Policy and Practice, Ningbo University, Ningbo 315000, China)

  • Xiaobei Yang

    (School of Tourism Management, Henan Finance University, Zhengzhou 450046, China)

  • Huizhen Long

    (School of Tourism and Hospitality Management, Hong Kong Polytechnic University, Hong Kong, China)

  • Fengrui Zhang

    (College of Life Sciences, Sichuan Agricultural University, Ya’an 625014, China)

  • Qin Xin

    (Faculty of Science and Technology, University of the Faroe Islands, Vestarabryggja 15, FO 100 Torshavn, Faroe Islands, Denmark)

Abstract

This paper aims to realize the planning of resource utilization and development of rural industries endowed by digitalization under entrepreneurship. First, the global classic practical experience of digitizing rural industries is studied, and the development model of existing rural industries is captured from the perspective of entrepreneurship. Second, the influencing factors of rural industrial development are extracted, the structure of resource development is analyzed, and a Neural Network (NN) model of industrial development aiming at expected per capita annual income is established. In addition, a Genetic Algorithm (GA) is introduced to learn the weights of influencing factors in the model. The structure of the NN is determined through extensive experiments. Finally, conclusions are drawn through the simulation and experiment of NN and GA. Tourism, infrastructure, and transportation planning have weights of 7.79, 5.6, and 6.4, respectively, and these three sectors should be vigorously developed. In the future, the weight values of these factors can be used for reference, and the development of various aspects can be refined. This paper clarifies the core of industrial development in rural revitalization based on the perspective of entrepreneurship. The problem of how to realize the optimal utilization of resources is solved scientifically and rationally through the mathematical model. The introduction of deep learning algorithm models provides data support for resource allocation and industrial planning in the process of digital empowerment of traditional rural industries, which is of great value and significance for exploring digital models for rural industry development.

Suggested Citation

  • Suwei Gao & Xiaobei Yang & Huizhen Long & Fengrui Zhang & Qin Xin, 2023. "The Sustainable Rural Industrial Development under Entrepreneurship and Deep Learning from Digital Empowerment," Sustainability, MDPI, vol. 15(9), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7062-:d:1130711
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    References listed on IDEAS

    as
    1. Sept, Ariane, 2020. "Thinking Together Digitalization and Social Innovation in Rural Areas: An Exploration of Rural Digitalization Projects in Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(2), pages 193-208.
    2. Heping Ge & Bowen Li & Decai Tang & Hao Xu & Valentina Boamah, 2022. "Research on Digital Inclusive Finance Promoting the Integration of Rural Three-Industry," IJERPH, MDPI, vol. 19(6), pages 1-18, March.
    3. Wei Chen & Quanzhong Wang & Hong Zhou, 2022. "Digital Rural Construction and Farmers’ Income Growth: Theoretical Mechanism and Micro Experience Based on Data from China," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    4. Olga Tikhomirova, 2020. "Entrepreneurial innovative network and the design of socio-economic neural system," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 9(2), pages 80-102, April.
    5. Rocío Blanco-Gregory & Leonor Elena López-Canto & María Victoria Sanagustín-Fons & Violante Martínez-Quintana, 2020. "Agroecological Entrepreneurship, Public Support, and Sustainable Development: The Case of Rural Yucatan (Mexico)," Land, MDPI, vol. 9(11), pages 1-24, October.
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    Cited by:

    1. Han Hao & Chenyang Liu & Ling Xin, 2023. "Measurement and Dynamic Trend Research on the Development Level of Rural Industry Integration in China," Agriculture, MDPI, vol. 13(12), pages 1-16, December.

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