Title: The algorithm of college students' physical fitness analysis based on data mining under the background of online courses

Authors: Yi Lu

Addresses: Department of Basic Education, Qingdao Vocational and Technical College of Hotel Management, Qingdao, 266100, China

Abstract: To promote the healthy advancement of college students' physical fitness and accurately and scientifically analyse the students' physical fitness test results, this research proposes a data mining based college students' physical fitness analysis algorithm. The physical fitness data of colleges, classes, and individuals is analysed, and the corresponding suggestions are proposed. The results show that there are 5,128 valid data points for boys and 7,812 valid data points for girls obtained by optimising K-means. In the case study, radar analysis was used. The results showed that the students' standing long jump was the strength of the class, and the lower limb strength developed well; The rest are all yellow areas, which shows that their development is balanced. If the score of the students' 50-metre run is in the orange area, some special training can be carried out.

Keywords: data mining; decision tree; physical fitness; optimisation algorithm; platform design.

DOI: 10.1504/IJCSYSE.2025.149205

International Journal of Computational Systems Engineering, 2025 Vol.9 No.2/3/4, pp.121 - 129

Received: 11 Apr 2023
Accepted: 11 Jun 2023
Published online: 20 Oct 2025 *

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