Comprehensive Application of Mixture Density Network Model and Action Feature Screening Strategy in the Choreography of Different Dance Styles

Intelligent science and technology are constantly changing, and society and people’s lives are inseparable from science and technology, affecting the transformation of artistic creation. This study constructed a choreography model based on a mixed-density network and action feature filtering strategy to help solve the disadvantages of traditional choreography. The model combines the long short-term memory (LSTM) network and mixed-density network to generate dance movements. First, the LSTM gating mechanism was used to learn the characteristics of human dance movements and obtain the conversion rules of various poses. The mixed-density network was introduced to compensate for the uncontrollable probability distribution in LSTM. In addition, during action generation, the experiment focused on the continuity between adjacent actions to screen the generated dance actions according to the diversity of dance actions and to enhance the continuity and authenticity of dance. Finally, an experiment was conducted on the spatial feature extraction and music feature matching of the model to achieve the goal that the model can generate different styles of choreography. The test user gave a score of more than four points to the final choreography effects of different styles, showing that the model can achieve a better intelligent choreography effect.