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.

In the context of heritage tourism development, accurately identifying the preferences of new-generation tourists is critically important. Based on dual-process theory, this paper combines questionnaire surveys with neuroscience (ERP) techniques to construct an “explicit–implicit” dual-pathway model, using four types of heritage landscapes in Jingdezhen as examples to investigate the preference formation mechanisms of Generation Z. Research findings indicate that religious and traditional folk landscapes receive higher explicit preferences; “Place imagination-awe” constitutes a chain mediation, with visual aesthetics positively moderating its pathway and influence preferences; Preferred landscapes trigger stronger P200 and LPP responses, while non-preferred landscapes elicit higher N200, presenting neural processing features such as attention capture and conflict detection. The research innovatively introduces ERP technology to heritage perception studies, promoting the interdisciplinary integration of heritage science and neuroscience, and provides empirical support for heritage site planning and neuromarketing targeting youth groups.