A Study on Semantic Classification of Guangxi Ethnic Folk Dance Movements Incorporating Deep Learning

Abstract

Abstract In recent years, the development of Guangxi’s national folk dance has been on the rise and has gained much attention. The research of Guangxi’s national folk dance is currently in a booming period. The research is based on deep learning theory, using stack denoising autoencoder and convolutional depth Boltzmann mechanism to build a SdAE-CDBM model for dance movement classification. The dance movements are recognized and detected by using feature mining and extraction of dance movements in Guangxi folk dance videos. The SdAE-CDBM model of this paper is compared with other classification models in terms of semantic classification accuracy of dance movements to explore the classification performance of the SdAE-CDBM model proposed in this paper. The average F1 values of the SdAE-CDBM model in the classification of the seven types of dance movements are 86.77%, 88.54%, and 90.18%, respectively, which are the maximum values among the movement classification models. The SdAE-CDBM model was able to achieve the highest classification accuracy and the fastest classification convergence speed among all classification models. When it comes to classifying dance movements semantically, the SdAE-CDBM model achieves a classification accuracy of 70.28%, which is significantly superior to other classification models. The SdAE-CDBM model in this paper is highly effective in the semantic classification of dance movements, as evidenced by this.