The effect of fitness qigong exercise on college students’ physical fitness based on big data technology

Abstract

In recent years, the physical quality of college students has been declining year by year, and it is important to study the influence of fitness qigong exercise on physical fitness. In this paper, we study the principle of the CART decision tree algorithm under big data technology, sort out its algorithm steps, and propose the optimization based on the genetic algorithm to improve the accuracy of the CART algorithm by using two-layer GA for the disadvantage that CART decision tree is easy to fall into local optimum. Then, the GA-CART algorithm is used to explore the relationship between college students’ physical fitness and physical function changes and fitness qigong exercise. After 12 weeks of performing fitness qigong exercise, the BMI of female college students decreased by 2.98 on average, the BMI of male college students decreased by 2.57 on average, and the Velvec index decreased by 0.286 on average. Regarding the effect of physical function, the heart rate of female college students decreased by 2.11, 2.66, 5.97, and 12 weeks after 3, 6, 9, and 12 weeks of fitness qigong exercise on average, respectively. 6.31 beats/minute, while male college students decreased by 2.18, 1.81, 4.97, and 5.51 beats/minute, respectively. The study based on big data can provide more scientific guidance for college students to participate in fitness qigong exercises and help them to develop their overall physical fitness.

Keywords

• big data technology
• ga-cart algorithm
• local optimum
• fitness qigong exercise
• data mining.
• 47n50