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

Jinming Mou, Xiaohong Chen (), Wenhao Du () and Jiarui Han
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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

Sustainability, 2025, vol. 17, issue 3, 1-25

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.

Keywords: rural tourism system resilience; driving factors; LSTM neural network; simulation optimization; Heilongjiang Province (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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