Analysis on Organizational Structure System Model of China’s Sports Management System Based on KNN Algorithm

• Published in: Mobile information systems Vol. 2022; pp. 1 - 10
• Main Authors: Ren, Jie, Du, Congxin
• Format: Journal Article
• Language: English
• Published: Amsterdam Hindawi 31.07.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Classification; Classifiers; Economics; Electric power distribution; Governmental reform; K-nearest neighbors algorithm; Organizational aspects; Organizational structure; Power; Power measurement; Society; Sports; Sports management; Support vector machines; Trends; United States > US; China
• ISSN: 1574-017X; 1875-905X; 1875-905X
• DOI: 10.1155/2022/7048312

The national sports management function requires the government to sincerely help sports associations, cultivate intermediary organizations and sports markets, and urge them to gradually realize autonomy, so as to promote the socialization of national sports. The organizational structure reform of China’s sports management system needs not only a clear goal but also a new way to achieve it, so that the reform route is clear and the reform process is supported. Therefore, sports public service has entered the vision of China’s leisure education reform. In this paper, KNN (K-Nearest Neighbor) algorithm is used to establish the power measurement model of organizational structure individuals, quantitatively analyze the power distribution of organizational structure, and describe the nonlinear relationship between the power distribution of organizational structure individuals and organizational hierarchy. On this basis, aiming at the shortcomings of KNN algorithm when the sample distribution is unbalanced, a penalty mechanism is added and improved. The results show that under the condition of unbalanced samples, the classification effect is obviously improved, which is about 6% compared with KNN classifier and about 3% to 4% compared with SVM (support vector machine) classifier. Conclusion. The improved algorithm achieves high classification accuracy on the basis of good robustness.