Evaluation of Dragon and Lion Dance Teaching Actions and Digital Sports Intangible Cultural Heritage Inheritance Based on Hypergraph Convolution

Given that traditional graph structures make it difficult to capture complex interaction information between entities, this study adopts a hypergraph model to represent multimodal and heterogeneous data, to adapt to the complexity of dragon and lion dance movements. This study proposes a new method based on a hypergraph convolutional network (HGCN) for the inheritance and teaching action evaluation of the intangible cultural heritage of the dragon and lion dance. This method constructs the HGCN model, combined with a self-attention mechanism to accurately evaluate action details and promote its inheritance in the digital age. The results show that the HGCN algorithm incorporating the attention mechanism exhibits excellent performance, the accuracy achieves 0.941, the error rate reduces to 0.333, an evaluation efficiency improved by 400%, and user satisfaction increases to 0.900. These results not only validate the efficiency and accuracy of the model but also demonstrate its potential to improve the teaching and inheritance efficiency of the dragon and lion dance. This study not only provides a new technological means for the digitization of intangible cultural heritage in sports but also opens up new paths for the modern teaching and inheritance of traditional sports projects.