Inter-Provincial Similarities and Differences in Image Perception of High-Quality Tourism Destinations in China

Wudong Zhao, Jiaming Liu (), He Zhu, Fengjiao Li, Zehui Zhu and Rouyu Zhengchen
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Wudong Zhao: Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Jiaming Liu: Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
He Zhu: Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Fengjiao Li: School of Architecture, Tsinghua University, Beijing 100084, China
Zehui Zhu: Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Rouyu Zhengchen: Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Land, 2025, vol. 14, issue 10, 1-23

Abstract: With the rapid development of China’s tourism industry, the homogenization of regional tourism images has become a growing concern. To address this, this study quantifies the similarities and differences in tourism image perception across China’s 31 provinces, focusing on 350 5A-level destinations, analyzing 757,046 tourist reviews collected from Ctrip.com in 2024. Using a three-dimensional framework (cognitive, affective, and overall image), we analyze social media data through natural language processing, random forest regression, and social network analysis. Key findings include the following: (1) most comments are positive, with Jiangsu and Chongqing showing high cognitive image similarity but low overall similarity; (2) cognitive image significantly impacts affective image, especially through unique tourism resources; (3) an inter-provincial similarity–difference matrix reveals significant perceptual differences among provinces. This study provides a novel methodological approach for multidimensional image evaluation and offers crucial empirical insights for regional policy-making aimed at optimizing land and tourism resource allocation, balancing regional disparities, and promoting sustainable land use and development across China.

Keywords: tourism image perception; high-quality tourism destinations; big data analysis; random forest regression; social network analysis; inter-provincial; Python (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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