School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China.
College of Physical Education and Sports, Beijing Normal University, Beijing 100875, China.
J Environ Public Health. 2022 Jul 30;2022:2587169. doi: 10.1155/2022/2587169. eCollection 2022.
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.
在韵律体操资源处理中，存在教学资源混淆、缺乏个性化等效率低下的问题。为了提高健康教学资源数据的可及性，如视频和文档，本研究提出了一种基于云计算的个性化韵律体操教学资源分类算法，以促进健康。首先，基于云计算技术设计个性化韵律体操教学资源数据库，并对数据库中的教学资源进行预处理，得到元样本集。然后，采用信息获取方法选择教学资源的特征，建立向量空间模型计算教学资源的相似度。最后，采用距离加权 k-NN 方法对促进健康的教学资源进行分类。实验结果表明，所提算法的分类精度高、召回率高、F 值高，验证了算法的有效性。
