Analysis of Physical Education Teaching and Prediction of Students' Sports Performance Based on Artificial Intelligence Algorithms

Taking students' sports performance as the research object, this study launched a multidimensional exploration through systematic data analysis and intelligent algorithm modeling. First, descriptive statistics were used to characterize the data of sports performance-related indicators, calculate the mean, median and extreme value distribution of each parameter, and reveal the overall characteristics of the data set. On this basis, the correlation analysis technique was used to quantitatively assess the influence weights of each indicator on physical education performance, and it was found that physical education performance scores, course participation rates and course grades constituted the core influence dimensions, and their correlation strengths were significantly higher than those of other observed indicators. For the construction of the prediction model, the Sparrow Search Algorithm (SSA) was innovatively introduced to optimize the parameters of the random forest model, and the SSA-RF hybrid model was established to realize the synergistic optimization of feature selection and hyper-parameter tuning. The experimental results show that the model achieves 99.75% and 96% prediction accuracies in the training set and test set, respectively, and its confusion matrix shows a balanced recognition ability for all types of samples. The study further explores the application value of AI technology in the field of physical education, and confirms that the intelligent algorithm can not only improve the prediction accuracy of sports performance, but also provide data support for the development of personalized training programs and curriculum content optimization. By constructing a whole-process framework of data analysis-model optimization-education application, this study provides an operable methodological system for the implementation of intelligent physical education, and its research results are of great practical significance for promoting the digital transformation of physical education teaching and establishing an accurate evaluation mechanism based on big data.