To explore the feasibility of using AI (artificial intelligence) three-dimensional motion analysis for analyzing the influencing factors on the distance of ski jumping and optimizing athletes’ technical movements, a study was conducted during the 2022 FIS Continental Cup Beijing event. Sixteen athletes’ take-off phase motions were captured within a fixed range using AI-based three-dimensional motion analysis system. This system automatically parsed the videos to obtain biomechanical parameters of the athletes’ take-off phase. By comparing the correlation coefficients and differences between manually processed data and AI-generated data of the three-dimensional coordinates of body joints over time, the validation of the equipment for ski jumping was conducted. The multiple correlation coefficient was found to be greater than 0.91, with an average difference value of less than 1.48 cm, indicating the reliability of the system. Furthermore, a comparison was made between the technical parameters of high-level foreign athletes and domestic athletes. Using t-tests and Pearson correlation coefficient analysis, the relationship between body posture parameters during the take-off phase and sports performance was examined. The results reveal correlations between take-off speed and angles of ankle and knee during the take-off phase, and between the final score and ankle angle during take-off, suggesting that Chinese athletes should focus on achieving full extension during take-off and timing their jumps appropriately to significantly enhance sports performance. Overall, the system demonstrated precise feedback for ski jumping technique analysis. Additionally, it enabled the acquisition of biomechanical parameters from world champion athletes to construct a champion model, providing valuable training references for Chinese athletes.