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It is difficult for traditional generation methods to accurately match dance movements and dance music in the automatic generation of dance. This paper introduces the technologies related to deep learning (DL) and proposes a system for automatic dance generation based on DL. The dance generation algorithm is the system’s linchpin. The first step is to extract dance and audio characteristics. Identifying the skeletal data of the dance movement is crucial to the extraction process. This paper employs an enhanced 3D convolutional neural network to determine the dance movement skeleton sequence. In the second step, a generative model capable of generating dance moves that precisely match the dance music is designed. The experimental results demonstrate that the dance movement recognition method proposed in this paper is highly accurate, that the dance generation method is very close to the actual dance movement, that the music matching rate is more accurate, and that the dance generation effect is favorable.

The traditional teaching system uses local area network to realize online teaching, which leads to high stability and hardware load of the teaching system and makes it difficult to meet the teaching demand. To solve the above problems, a dance movement distance learning system based on wireless network communication technology is designed. On the B/S architecture, the hardware control module and communication module of the system are designed. The fuzzy set principle is used to evaluate the students’ dance cognitive ability, so as to personalize the recommended teaching contents. The teaching video is compressed according to H.264/AV compression standard to reduce the system transmission and processing load. The system functionality test results show that the maximum transmission packet loss rate of the designed system is 8.3%, and the lost data does not interfere with teaching, has low computer memory consumption, and has superior performance.