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.