The impact of AI-generated technologies-driven digital cultural heritage platforms on users’ offline cultural participation intentions

AI-generated technologies are shifting user experiences in digital cultural heritage from passive reception to immersive interaction, yet how online experiences influence offline participation remains unclear. This study applies the Net Valence Model (NVM) to construct a dual-path framework linking perceived benefits, perceived risks, and behavioral intentions. Using the Cloud Tour of Dunhuang platform as a case, 986 survey responses and interviews were analyzed via structural equation modeling (SEM) and artificial neural networks (ANN). Results show perceived benefits significantly enhance offline participation intentions, with creative design and narrative experience as primary drivers, while perceived risks—mainly privacy and emotional concerns—suppress intentions. ANN reveals nonlinear weight structures, indicating narrative design is underestimated in SEM. Qualitative analysis further identifies latent factors, such as behavioral accessibility, deepening understanding of user behavior mechanisms. This study extends NVM’s applicability and provides insights for content design and risk governance in AIGC-driven heritage platforms.