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With the rapid development of tourism, how to scientifically analyze and optimize the spatial distribution of tourism landscape resources has become an important issue to promote the sustainable development of tourism. Most existing studies focus on the analysis of a single factor and lack a systematic method that comprehensively considers the spatial distribution characteristics of landscape resources. In order to solve this problem, this study introduces a method for analyzing the spatial distribution characteristics of tourism landscape resources based on fuzzy mathematics and Dijkstra algorithm. Firstly, the membership function in fuzzy mathematics is used to quantitatively analyze the spatial characteristics of different landscape resources, and the influencing factors of various landscape resources are comprehensively considered in combination with the fuzzy comprehensive evaluation model. Next, the Dijkstra algorithm is applied to calculate the shortest path and convenience between tourist attractions, further optimize the spatial layout of the attractions, and improve tourists’ travel efficiency and tourism experience. Finally, the experimental results show that through the optimized spatial layout of scenic spots, the coefficient of variation of scenic spot distribution is reduced from 0.75 to 0.45, and the standard deviation is reduced from 1.2 to 0.8, which significantly improves the spatial balance of scenic spots; at the same time, the optimized network not only improves the connectivity between scenic spots but also provides tourists with more efficient travel options; by reasonably optimizing the layout of scenic spot resources and traffic flow lines, the overall experience of tourists has been effectively improved, and the reception capacity of scenic spots and tourist satisfaction have also been enhanced. The experimental data verified the practical application value of this method in improving scenic area resource optimization and tourist flow management, and provided important decision-making support for the sustainable development of tourist attractions.

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Analysis of Spatial Distribution Characteristics of Tourism Landscape Resources Based on Fuzzy Mathematics and Dijkstra Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Caifeng Ma

摘要

With the rapid development of tourism, how to scientifically analyze and optimize the spatial distribution of tourism landscape resources has become an important issue to promote the sustainable development of tourism. Most existing studies focus on the analysis of a single factor and lack a systematic method that comprehensively considers the spatial distribution characteristics of landscape resources. In order to solve this problem, this study introduces a method for analyzing the spatial distribution characteristics of tourism landscape resources based on fuzzy mathematics and Dijkstra algorithm. Firstly, the membership function in fuzzy mathematics is used to quantitatively analyze the spatial characteristics of different landscape resources, and the influencing factors of various landscape resources are comprehensively considered in combination with the fuzzy comprehensive evaluation model. Next, the Dijkstra algorithm is applied to calculate the shortest path and convenience between tourist attractions, further optimize the spatial layout of the attractions, and improve tourists’ travel efficiency and tourism experience. Finally, the experimental results show that through the optimized spatial layout of scenic spots, the coefficient of variation of scenic spot distribution is reduced from 0.75 to 0.45, and the standard deviation is reduced from 1.2 to 0.8, which significantly improves the spatial balance of scenic spots; at the same time, the optimized network not only improves the connectivity between scenic spots but also provides tourists with more efficient travel options; by reasonably optimizing the layout of scenic spot resources and traffic flow lines, the overall experience of tourists has been effectively improved, and the reception capacity of scenic spots and tourist satisfaction have also been enhanced. The experimental data verified the practical application value of this method in improving scenic area resource optimization and tourist flow management, and provided important decision-making support for the sustainable development of tourist attractions.