The method of grouping and classifying music curriculum teaching resources in the context of double reduction

Abstract

Abstract A subsumption classification method is proposed to improve the classification accuracy of teaching resources in the music curriculum through a double reduction policy. Subject words are selected from high-similarity and high-frequency word sets, and a subject word tree is constructed using an automatic tree construction method based on a probabilistic latent semantic analysis algorithm. To complete the subsumption classification of teaching resources, an improved multi-graph kernel convolutional network is employed to group tree leaf nodes. According to the classification evaluation results, the recall, accuracy, and F1 values are 90%, 96.48%, and 88.81%, respectively, and the macro F1 value is as high as 81.59%. It can be seen that the method can effectively classify the teaching resources of music courses with the best effect of subsumption classification, which helps to improve the appropriateness and adequacy of teaching resources utilization.