A user-demand-driven neuro-symbolic framework for sustainable Xuan paper intangible cultural heritage preservation

Abstract Digital preservation of intangible cultural heritage (ICH) often faces challenges of fragmented knowledge and misalignment with users’ cognitive needs. Targeting the Traditional Production Skills of Xuan Paper, this study proposes a user-demand-driven neuro-symbolic cognitive enhancement framework for ICH knowledge graphs (NSCEF-ICHKG). The framework first constructs a “user demand-information density” (UD-ID) mapping matrix by integrating the analytic hierarchy process (AHP) with cognitive load theory (CLT). Subsequently, it develops a Neuro-Symbolic Xuan Paper Ontology (NS-XuPOnto): the symbolic layer, extending CIDOC CRM and embedding SWRL rules, ensures cultural logic, while the neural layer, leveraging large language models (LLMs), resolves terminological ambiguity and aligns multimodal semantics. Finally, it generates adaptive, cognitively-graded views via Neo4j. Experiments demonstrate that the implemented XPKG achieves superiority over traditional baselines in entity retrieval performance ( F 1 = 84.6%, p < 0.01) and effectively reduces users’ cognitive load while enhancing task efficiency, as measured by NASA-TLX. This framework provides a transferable, human-centered approach for the sustainable digital preservation of craft ICH.