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Simulation Research on the Optimization of Rural Tourism System Resilience Based on a Long Short-Term Memory Neural Network—Taking Well-Known Tourist Villages in Heilongjiang Province as Examples

Author

Listed:
  • Jinming Mou

    (College of Geographical Science, Harbin Normal University, Harbin 150025, China)

  • Xiaohong Chen

    (College of Geographical Science, Harbin Normal University, Harbin 150025, China)

  • Wenhao Du

    (College of Geographical Science, Harbin Normal University, Harbin 150025, China)

  • Jiarui Han

    (Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China)

Abstract

Taking well-known tourist villages in Heilongjiang Province as the research object, we constructed a rural tourism system resilience assessment framework with the dimensions of “environment, institution, economy, society, and culture”. Using a geographical detector to analyze driving factors, an LSTM neural network model was constructed to predict the evolution trend of the rural tourism system resilience of these villages. The resulting insights included the following: ① The rural tourism system resilience of the well-known tourist villages in Heilongjiang Province is at a medium level, with a relatively good degree of development in the environmental dimension and the lowest degree in the economic dimension. ② The existence of financial support, water supply guarantee, domestic waste treatment, livestock manure treatment, and tourism development satisfaction are core driving factors for rural tourism system resilience; there is a non-linear or two-factor enhancement effect among these factors, and the interaction between domestic waste treatment and tourism development satisfaction has the strongest influence, while policy support particularly improves rural tourism system resilience and interacts most frequently with other driving factors. ③ Compared to the backpropagation (BP) neural network, the long short-term memory (LSTM) neural network has better stability and prediction accuracy.

Suggested Citation

  • Jinming Mou & Xiaohong Chen & Wenhao Du & Jiarui Han, 2025. "Simulation Research on the Optimization of Rural Tourism System Resilience Based on a Long Short-Term Memory Neural Network—Taking Well-Known Tourist Villages in Heilongjiang Province as Examples," Sustainability, MDPI, vol. 17(3), pages 1-25, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1305-:d:1584627
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    References listed on IDEAS

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    1. Shuchang Li & Wei Song, 2023. "Research Progress in Land Consolidation and Rural Revitalization: Current Status, Characteristics, Regional Differences, and Evolution Laws," Land, MDPI, vol. 12(1), pages 1-24, January.
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