Decoding Tourists’ Landscape Perception Preferences in Historical and Cultural Heritage Parks Through Social Media Images: A Dual-Task Deep Learning Framework

Historical and cultural heritage parks are important spaces for heritage conservation, cultural transmission, and public recreation. However, conventional landscape perception research mainly relies on questionnaires and interviews, making it difficult to capture tourists’ visual preferences at scale. This study proposes a dual-task attention-enhanced ResNet framework based on social media user-generated content (UGC) images to investigate tourists’ landscape perception preferences in historical and cultural heritage parks. Using Yellow Crane Tower Park, Guqintai, and Guishan Scenic Area in Wuhan, China, as case studies, 6221 images were collected from Ctrip, Xiaohongshu, Weibo, and field surveys. The framework jointly performs landscape element detection and aesthetic attribute classification through shared feature representation and attention mechanisms. The proposed model achieved a composite Macro-F1 score of 0.7641, demonstrating robust classification performance. The results show that Buildings and Structures exhibited the highest average prediction probability (0.5824), while Spatial Legibility was the dominant aesthetic attribute (0.5322), indicating a perception pattern characterized by cultural-symbol prominence and enhanced spatial cognition. Vegetation and road networks were positively associated with spatial mystery, whereas excessive visual complexity reduced spatial legibility. These findings demonstrate the value of combining deep learning with social media image analytics for cultural landscape perception research and provide practical insights for landscape planning, heritage conservation, and tourism management.