In order to make my country’s youth health management more scientific, more standardized, and more effective, it is necessary to conduct in-depth research on the management of youth physical health promotion. Through the investigation and analysis of the physical health data of adolescents in my country, this paper proposes that the results of health intervention training as part of the empirical research on the construction of adolescent health big data management service system can effectively improve the relationship hypothesis of the physical health of adolescents and by selecting the example of our country’s “Adolescent Physical Health Data Analysis in 2020” for regression analysis. The research results show that the theory of adolescent physical health promotion can improve the physical health of adolescents by interfering with students’ physical exercise. In the processing of data, GBDT is suitable when the training set is relatively large, and as the sample size increases, the accuracy rate can reach 79.79%. In terms of the classification accuracy of male sitting forward bending promotion, the accuracy of the RF method is higher than that of GBDT. In terms of the promotion classification effect of boys’ 1000 m running, the RF method achieved the highest promotion accuracy rate of 77.62%. In the male pull-ups to promote the classification effect, when the proportion of the training set is 60%, the RF method gets the highest accuracy rate, which is 92.04%. The results of the classification effect for girls standing long jump promotion show that the classification accuracy rate for girls standing long jump promotion is between 51% and 56%. When the training set is less than 60%, the RF method is the best, the highest is 53.93%, and the rest is the GBDT method, the highest is 55.46%; in Macro-F1, the RF and GBDT indicators have their own advantages. In the promotion of the classification effect on the final fitness level of girls, the accuracy rates of RF and GBDT methods range from 90% to 96%, and the accuracy rates of the NN method range from 80% to 87%; when the practice rate reaches 80%, the GBDT method achieves the highest accuracy rate of 95.06%; on the Macro-F1 index, the GBDT method is obviously the best.
Health data analysis based on multi-calculation of big data during COVID-19 pandemic