Research on digital inheritance system and action semantic analysis of Hunan flower-drum opera based on deep learning

Yi-Fu Liao ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 4, 295-311

Abstract: This study explores the digital transmission and protection of Hunan Flower-Drum Opera, a key intangible cultural heritage asset, through action semantic analysis combined with deep learning approaches. The research establishes a multimodal acquisition setup, develops an enriched Spatial-Temporal Graph Convolutional Network with an attention mechanism, creates a traditional Chinese opera-oriented semantic classification system, and constructs a digital heritage platform with an integrated knowledge graph. The proposed approach achieves a 92.6% action recognition rate and an 88.3% consistency rate in semantic analysis, showing a 17.5% improvement over traditional models. The 33-point BlazePose architecture achieves sub-millimeter accuracy in movement tracking, while the knowledge graph contains 3,427 nodes with 12,856 weighted edges. The interactive learning environment demonstrates significant improvement in learning outcomes with a 31% reduction in learning time while preserving cultural authenticity. The system has been successfully implemented at three centers specializing in Flower-Drum Opera transmission, offering a unique solution for the digital conservation of traditional Chinese performing arts.

Keywords: Action semantic analysis; Deep learning; Digital heritage preservation; Hunan flower-Drum Opera; Knowledge graph spatial-temporal graph convolutional networks. (search for similar items in EconPapers)
Date: 2025
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