In order to improve the accuracy and timeliness of folk dance movement recognition, this paper proposes an improved MCM-SVM recognition model to recognize the lower limb human motion of ethnic dance in rural areas based on sensors. In order to recognize these actions, the SVM algorithm is used to identify the current action, and the MCM is used to optimize the recognition result. The experimental results show that the proposed improved model achieves higher recognition rate compared to the SVM algorithm for the recognition of different dance moves. The average recognition rate exceeds 93%, and the average recognition time is about 0.6 ms, which verifies the effectiveness of the proposed model. The proposed model will provide guidance and practicality for the design and construction of future dance movement recognition systems.

In order to improve the accuracy and timeliness of folk dance movement recognition, this paper proposes an improved MCM-SVM recognition model to recognize the lower limb human motion of ethnic dance in rural areas based on sensors. In order to recognize these actions, the SVM algorithm is used to identify the current action, and the MCM model is used to optimize the recognition result. The experimental results show that the proposed improved model achieves a higher recognition rate compared to the SVM algorithm for the recognition of different dance moves. The average recognition rate exceeds 93%, and the average recognition time is about 0.6 ms, which verifies the effectiveness of the proposed model. The proposed model will provide guidance and practicality for the design and construction of future dance movement recognition systems.

Because of the special ecological environment and humanistic atmosphere in new rural areas, excellent regional dance art has been created. Through computer-aided technology, the essence of dance art in rural areas can be reconstructed and displayed. Therefore, based on 3D image reconstruction technology, this paper obtains the dance data of southeast Guangxi and puts forward the dance display scheme of new rural areas. Through acquisition of image information and image matching algorithm, the dance pose is estimated, and the extracted dance sequence is simplified by 3D reconstruction and mapped by texture. In addition, extraction effect of data set, comparison of dance similarity, and user authenticity score were used to test the five types of dance, which provides ideas for the inheritance and development of traditional folk dance culture.