YiChen DuThis email address is being protected from spambots. You need JavaScript enabled to view it. 

EDNA Joint Institute, China Academy of Art, Hangzhou 310000, Zhejiang, China


 

Received: December 20, 2025
Accepted: January 31, 2026
Publication Date: February 26, 2026

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202608_31.037  


This study proposes an intelligent restoration and immersion system for digital intangible cultural heritage, addressing the lack of multimodal data coordination. It uses a Multimodal Graph Convolutional Network (MM-GCN)tofuse images, craft texts, and dynamic action data, with spatiotemporal alignment and adaptive weight allocation to optimize modal contributions. The dual-channel architecture integrates visual restoration via MS-GANwith semantic constraints and tactile feedback through a mechanical model, achieving 4K/60fps real-time rendering. The system achieves an average structural similarity of 0.915, a feature extraction rate of 44.2fps, and a tactile feedback delay under 0.85ms. This algorithm provides a high-precision, low-latency solution for the digitization of intangible cultural heritage, promoting the living inheritance of cultural skills.


Keywords: Digital Intangible Cultural Heritage Art Restoration; Immersive Experience System; Multimodal Fusion; MM-GCN;MS-GAN


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