Analysis of Data Mining Modes of Gymnastics Training Based on AHP Method

Abstract

Under the background of the information age, data analysis has become an essential work of people. Based on the number of trampoline athletes in the gymnastic event, this paper explores data mining modes of gymnastics training data. First, based on the Markov chain model, grading is respectively given to the number of international athletes and the number of national athletes, and roughly grade prediction is given to the number of athletes. Second, the grey prediction model is used to predict the cumulant of the number of international athletes and the number of national athletes, and give out the relationship between the number of athletes and the time, and predict the specific values. Finally, the Markov chain model and the grey prediction model are evaluated by the use of AHP method from the following three aspects: the simplicity of calculation, the rationality of results and the guiding significance of practical training. The results show that the grey prediction mode is more suitable for data mining of gymnastics training.