Research on user satisfaction with AIGC assisted museum scenario design

In the context of digital transformation, artificial intelligence generated content (AIGC) technology provides an innovative path for museum smart scene design, but existing research lacks a user-centered systematic framework. This study uses questionnaire surveys and structural equation models (SEM) to explore the mechanism of AIGC technology adaptability, user demand fit, scene design innovation, technology acceptance and user satisfaction. The results show that user demand fit has the strongest direct impact on satisfaction, highlighting the "user-centered" design core; AIGC technology adaptability improves satisfaction through direct and indirect paths, verifying the mediating effect of the technology acceptance model (TAM); scene design innovation needs to transform value through technology acceptance to affect user satisfaction. The study constructs a closed-loop model of "demand drive-technology adaptation-scene innovation-acceptance conversion", and proposes a design strategy based on cognitive load balance, which provides a theoretical basis and practical path for museums to use AIGC technology to improve user experience, and promotes the paradigm shift of museums from "object-centered" to "people-centered".