AI–Human Collaborative Interpretation (AHCI)—A Methodological Framework for Human–Machine Collaboration in the Visualisation and Aesthetic Evaluation of Complex Cultural Heritage

Liwen Zhang, Yiqi Liu, Jingya Li, Yuexi Dong

This paper proposes the AI–Human Collaborative Interpretation (AHCI) human–machine collaboration methodological framework, offering a new research pathway for the digital interpretation of complex cultural heritage. The framework integrates multi-source data processing, formalised feature modelling, interpretable analysis and human–machine feedback mechanisms, translating the complex information of cultural heritage into an analysable and interactive structure. Building upon this, the Tianlai.China project serves as a case study for the multimodal data integration, aesthetic feature modelling and interactive visualisation of the Illustrated Catalogue of Famous Porcelains Through the Ages. The effectiveness of the collaborative mechanism is validated through expert–public–AI comparative experiments and human–AI feedback loop experiments. The main contributions of this paper include: proposing a methodological framework for human–machine collaboration in cultural heritage value assessment; constructing an interpretable formalised model of cultural features and a corresponding visualisation system; analysing the impact of AI explanations on expert cognition through empirical research; and exploring the transferability of this framework across different types of cultural heritage. Against this backdrop, the human–machine collaboration system developed by AHCI—comprising ‘data semantics–feature modelling–visual output–cognitive feedback’—possesses cross-domain applicability.