Article: Integrating deep learning and GIS technology for optimising rural tourism development paths Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.59 pp.70 - 97 Abstract: In response to the problems of homogenisation in rural tourism development and inefficient resource allocation, this study has designed a rural tourism development path optimisation method that integrates geographic information system and deep learning. Firstly, multiple sources of spatial data are integrated, and convolutional neural networks are used to automatically predict the potential of rural tourism development. Subsequently, using the potential spatial distribution as input, a mathematical model for path optimisation is constructed, and an improved deep reinforcement learning method is employed, incorporating local operators to perform neighbourhood search and iteratively improve the initial solution. The experiments show that the net benefit value achieved by the proposed method on the standard test set is 44.05, and the solution time is only 3.14 seconds, which is significantly better than the comparison algorithms, providing an effective solution for the optimisation of rural tourism development paths. Inderscience Publishers - linking academia, business and industry through research

Title: Integrating deep learning and GIS technology for optimising rural tourism development paths

Authors: Yahui Sun

Addresses: Zhengzhou Tourism College, Zhengzhou, 451464, China

Abstract: In response to the problems of homogenisation in rural tourism development and inefficient resource allocation, this study has designed a rural tourism development path optimisation method that integrates geographic information system and deep learning. Firstly, multiple sources of spatial data are integrated, and convolutional neural networks are used to automatically predict the potential of rural tourism development. Subsequently, using the potential spatial distribution as input, a mathematical model for path optimisation is constructed, and an improved deep reinforcement learning method is employed, incorporating local operators to perform neighbourhood search and iteratively improve the initial solution. The experiments show that the net benefit value achieved by the proposed method on the standard test set is 44.05, and the solution time is only 3.14 seconds, which is significantly better than the comparison algorithms, providing an effective solution for the optimisation of rural tourism development paths.

Keywords: rural tourism; optimisation of development path; deep learning; deep reinforcement learning; neighbourhood search.

DOI: 10.1504/IJICT.2026.153931

International Journal of Information and Communication Technology, 2026 Vol.27 No.59, pp.70 - 97

Received: 15 Feb 2026
Accepted: 24 Mar 2026
Published online: 08 Jun 2026 *

Title: Integrating deep learning and GIS technology for optimising rural tourism development paths

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