Cultural heritage tourism network analysis faces core challenges including dynamic high-order association modeling, multi-source heterogeneous data fusion, and multi-dimensional influence prediction. To address these issues, this study proposes DynHyperNet, a dynamic hypergraph network model integrated with multi-modal fusion and multi-task learning. The Dynamic HyperGNN module in DynHyperNet captures the temporal evolution of high-order correlations among cultural heritage sites, tourists, and supporting services, while the CLIP-based multi-modal fusion module fuses visual and textual features of heritage sites to enhance node representation learning. A multi-task collaborative framework is further designed to simultaneously optimize satisfaction prediction, route planning optimization, and other key tasks. Results demonstrate that DynHyperNet outperforms comparative models across multiple metrics: on the Cultural Tourism Dataset, it achieves 93.76% satisfaction prediction accuracy, 92.45% high satisfaction recall rate, and 3.89% route planning optimization MAPE for world cultural heritage sites. Despite its effectiveness, the model exhibits limitations in computational efficiency and adaptability to sparse data scenarios. Future work will focus on lightweight architecture optimization, sparse data adaptation, and integration of external dynamic factors to enhance practical applicability. This study provides a new technical framework for dynamic analysis and intelligent prediction of cultural heritage tourism networks, offering actionable insights for tourism management and sustainable development.
Wenhua et al. (2026) studied this question.