Design and Development of a Virtual Reality Framework for Digital Inheritance and Preservation of Intangible Heritage

Intangible cultural heritage preservation has been made progressively difficult owing to scattered documentation, loss of indigenous knowledge, and restricted accessibility for younger generations. Current solutions lean towards static digital collections or low-fidelity 3D reconstructions, which do not accurately record contextual interactions and immediate immersive exposures. Conventional solutions like rudimentary multimedia repositories and conventional VR visualization models do not have semantic connectivity, adaptive knowledge migration, and intelligent understanding of cultural context. To solve these problems, a Hybrid Graph Convolutional Network with Transformer-based Attention (HGCT-A) is designed, combining VR technology with graph-based relational learning and multi-head attention to promote cultural knowledge connection, interaction realism, and immersive learning. Experimental testing proves that the HGCT-A model has 97.8% accuracy and 96.4 % F1-score, outperforming traditional models. The comparative evaluation reiterates the superiority of HGCT-A in semantic preservation, dynamic adaptation, and user involvement.