Compared with image records, 3D dance scores can realize the demonstration and practice of classical dance teaching more accurately and efficiently. In this paper, the Li group skeletal representation model is used for Li algebra space mapping processing to learn action feature data, classify them, and complete classical dance action feature extraction. Then, the residual network model is used as the backbone network to construct a two-stream spatiotemporal residual network for action recognition by using two different temporal spans and fusing the results of the two-channel networks through side connections. Finally, the group intelligence algorithm group intelligence algorithm ACOR-SGD is designed to optimize the parameters of the convolutional neural network, and then the dance score generation model is integrated into the design of the innovative teaching method of classical dance for the application of the professional teaching of classical dance in colleges and universities. There is basically no significant difference between the pre-test scores of the experimental and control classes. After one semester of applying the innovative teaching method based on the generation of dance scores, the scores of the experimental class in the five items of basic pace mastery, movement fluency, stage presence, technical quality, and coordination of the music rhythm were improved by 4.6, 4, 4.28, 3.63, and 4.72 respectively, and the total score of 81.54 was much higher than that of the control class of 60.57 points posttest scores, and all were significant at the 0.01 level. This study explores innovatively and provides an effective path for integrating dance teaching with cutting-edge information technology, thus improving the quality and efficiency of dance teaching.
