Construction of a knowledge graph for dance teaching based on improved graph convolutional networks

Aiming at the inefficiency of retrieval and insufficient recommendation caused by the dispersion of dance multimodal resources, this study proposes a knowledge graph construction method based on improved GCNs. Integrating public dance libraries and teaching resources, the first dance knowledge graph DanceKG is constructed, covering entity relationships such as movements and styles. DRA-GCN is innovatively proposed to quantify the interaction frequency of nodes through dynamic weighting module and strengthen the characterization of long-tailed entities associated with complex movements by combining multi-head attention. Experiments show that DRA-GCN has a link prediction MRR of 0.72 (9% higher than traditional GCN) on 50,000 triad datasets, and the F1-value of movement classification is improved by 5%, which supports the movement recommendation and error correction of intelligent teaching system and promotes the digital development of dance education.