College Student Sports Training Features Analysis using Naive Bayes Classification

The recognition system with environment behaviors investigates college students' dynamic sports growth performance. In revolutionary technology sectors including autonomy, general object tracking, sports setting identification, and security, feature recognition is difficult. Therefore, in this paper, a Naive Bayes Algorithm with a statistically differentiated feature recognition system (NBASDS-FR) model has been proposed to recognize and classify college students' sports training attitudes. A new, statistically differentiated system is developed to identify the scene and isolate each object's in sports accurately. The texture features that connect key points, inclination, and detachment values are then extracted from each sports training. Naive Bayes achieves the feature recognition and classification. The experimental results of SDS-FR are tested over UIUC sport scene datasets and achieve the highest recognition and classification accuracy.