Research on Practical Paths for AI-enabled Cultural Heritage Conservation in Local Museums

The rapid advancement of cultural digitalization has positioned artificial intelligence as a key driver of change in cultural heritage conservation, promoting a shift from traditional physical preservation to more digital and intelligent approaches. As primary institutions for regional heritage conservation and public cultural services, local museums face multiple challenges in this process, including limited technical capacity for artifact preservation, insufficient exploration of cultural value, weak public engagement, and fragmented resource integration. To address these issues, a systematic review of the literature is conducted to develop a three-dimensional analytical framework encompassing physical preservation, cultural information mining, and public value dissemination. Taking Baoshan Museum as a case study, case-based analysis and practical synthesis are employed to examine its current constraints and emerging development needs in cultural heritage conservation. Building upon this analysis, the study proposes a set of AI-enabled technological applications, including high-precision digital acquisition, preventive intelligent monitoring, the construction of cultural heritage knowledge graphs, and immersive exhibition technologies. Furthermore, it establishes an integrated practical framework that combines preventive conservation, data-driven research, and intelligent communication. Finally, the study advances targeted optimization strategies from three aspects: the standardization of data resources, the development of interdisciplinary talent and institutional support mechanisms, and the promotion of social sharing. These efforts aim to provide a practical reference for enhancing the capacity of local museums in cultural heritage preservation and utilization.