Cultural Perception of Tourism Heritage Landscapes via Multi-Label Deep Learning: A Study of Jingdezhen, the Porcelain Capital

• Published in: Land (Basel) Vol. 14; no. 3; p. 559
• Main Authors: Cheng, Yue, Chen, Weizhen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 06.03.2025
• Subjects: Big data; Brand equity; China; Cities; Classification; Computational linguistics; Computer science; Cultural characteristics; cultural diversity; Cultural heritage; Cultural relations; Cultural tourism; Culture; Deep learning; Diversity indices; Economic development; heritage interpretation; Historic sites; Labels; Landscape architecture; Language processing; Mass tourism; multi-label classification; Natural language interfaces; Neural networks; Perception; Porcelain; Protection and preservation; Regional planning; Research methodology; Semantics; Social networks; Spatial analysis; Tourism; Tourism development; tourism heritage landscape; Travel industry; Urban development; User generated content; China
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land14030559

In the face of rapid progress in heritage preservation and cultural tourism integration, landscape planning in historic cities is pivotal to showcasing regional identities and disseminating cultural value. However, the complexity of cultural characteristic identification and the imbalance in planning often restrict the progress of urban development. Additionally, existing studies predominantly rely on subjective methods and focus on a single cultural attribute, highlighting the urgent need for research on diversified cultural perception. Using Jingdezhen, a renowned historic cultural city, as an example, this study introduces a multi-label deep learning approach to examine cultural perceptions in tourism heritage landscapes. Leveraging social media big data and an optimized ResNet-50 model, a framework encompassing artifacts, production, folk, and living culture was constructed and integrated with ArcGIS spatial analysis and diversity indices. The results show: (1) The multi-label classification model achieves 92.35% accuracy, validating its potential; (2) Heritage landscapes exhibit a “material-dominated, intangible-weak” structure, with artifacts culture as the main component; (3) Cultural perception intensity is unevenly distributed, with core areas demonstrating higher recognition and diversity; (4) Diversity indices suggest that comprehensive venues display stronger cultural balance, whereas specialized ones reveal marked cultural singularity, indicating a need for improved integration across sites. This research expands the use of multi-label deep learning in tourism heritage studies and offers practical guidance for global heritage sites tackling mass tourism.