LAUSR

In the processing of rhythmic gymnastics resources, there are inefficiency problems such as confusion of teaching resources and lack of individuation. To improve the health access to teaching resource data, such as videos and documents, this study proposes a cloud computing-based personalized rhythmic gymnastics teaching resource classification algorithm for health promotion. First, personalized rhythmic gymnastics teaching resource database is designed based on cloud computing technology, and the teaching resources in the database are preprocessed to obtain a meta-sample set. Then, the characteristics of teaching resources are selected by the information acquisition method, and a vector space model is established to calculate the similarity of teaching resources. Finally, the distance-weighted k-NN method is used to classify the teaching resources for health promotion. The experimental results show that the classification accuracy of the proposed algorithm is high, the recall rate is high, and the F-measure value is high, which verifies the effectiveness of the algorithm.