Data analysis of civil aviation flight students' physical training based on ID3 decision tree and BP neural network

This study aims to conduct effective analysis on the physical training data of civil aviation student pilots, thereby accurately analyzing the changing trends of students' physical fitness status and providing a scientific basis for optimizing training programs. First, the Iterative Dichotomiser 3 (ID3) decision tree algorithm is used to select key features from flight students' physical training data by calculating information gain and classify the data. Then, the Back Propagation Neural Network (BPNN) algorithm with strong nonlinear fitting capability is employed to further analyze and predict the classified data. This fusion method aims to combine the interpretability of decision trees with the pattern recognition ability of neural networks. The dataset from the Physical Training Center of Civil Aviation University of China is used to verify the algorithm performance. The results show that the classification accuracy of the ID3+BP algorithm is 92%, higher than other algorithms. The algorithm can accurately identify the impact of key features such as muscle strength and endurance level on physical fitness status, making the classification results closer to the actual physical fitness status and providing a reliable basis for personalized training programs. In the analysis of physical training data, the ID3+BP algorithm achieves a precision of 90%, a recall of 89%, and an F1 score of 89.5%, all outperforming other algorithms. In conclusion, this study provides a new method for analyzing physical training data of flight students, showing great potential for improving their physical fitness and flight safety.