Optimization Simulation of Match between Technical Actions and Music of National Dance Based on Deep Learning

In the match between technical movements and music of folk dance, the most important thing is to extract features effectively. DL algorithm is one of the most efficient methods to extract video features at present. In this study, the DL method is applied to the matching optimization of technical movements and music in folk dance. Using DL to train the corresponding relationship between the technical movements and music of national dance, the given dance movements and corresponding movements are adapted to the musical beat points. To better reflect the degree of correlation between music and movement changes, the change rate of feature value is used instead of feature value itself in correlation calculation. The matching degree between this method and genetic theory method and spatial skeleton timing diagram method is compared. The experiment shows that the matching method of technical movements and music of national dance optimized by DL can achieve 95.78% accuracy, and the matching synchronization of technical movements and music of national dance can reach 96.17%. Therefore, the method proposed in this study can fully reflect the synchronization of music and movement changes, and the optimized movement matching method matches the national dance technical movements—music matching quality is better. This study expands a new perspective for the research of dance and music matching technology. It has certain practical and theoretical significance.