Application of Fast Generation Algorithm in Motion Prediction Analysis System

In diversified and large-scale motion data forecasting, traditional forecasting methods often lack effective handling of complex changes, diversified changes, sudden factors, and dynamic changes, resulting in unsatisfactory accuracy and timeliness of forecasting results. This article proposed and studied a motion prediction analysis system based on fast generation algorithms. The fast generation algorithm has shown significant advantages in generating, filtering, modeling, and dynamic prediction in large-scale motion data due to its excellent dynamic processing ability, ability to handle complex problems, and advantage in real-time feedback. By verifying the relative error of predicting athlete heart rate load, the highest error in this article was 0.32%, and the lowest error was 0.11%; the highest error of heuristic algorithms was 8.73%, and the lowest error was 1.34%; the maximum error of traditional algorithms was 13,53%, and the minimum error was 3.56%. Compared to traditional algorithms and heuristic algorithms, the motion prediction analysis system based on fast generation algorithms had efficient prediction performance, and had significant advantages in the accuracy and speed of motion prediction. The optimization of sports forecasting analysis has a positive impact on athlete training and guidance.