In order to improve the performance prediction effect of athletes and provide better support for sports training, an athletes′ performance prediction model based on chaos theory and machine learning algorithm is proposed. The performance data of athletes is analyzed,and its change characteristic is found out with chaos theory. The athletes′ performance training sam-ple processed with chaos theory was modeled with neural network. The particle swarm algorithm is used to estimate the initial connection weights of neural network. The specific performance data of athletes is adopted to analyze the reliability of the model. The analysis results show that the model can find out the future change trend of the athletes′ performance data,improve the per-formance prediction accuracy of athletes,and its prediction result has a certain application value.%为了改善运动员成绩的预测效果,更好地为体育训练提供支持,提出基于混沌理论和机器学习算法的运动员成绩预测模型.对运动员成绩数据进行分析,并通过混沌理论找到运动员成绩的变化特点,通过神经网络对混沌理论处理后的运动员成绩训练样本进行建模,并采用粒子群算法在线估计神经网络初始连接权值,最后采用具体运动员成绩数据对模型的可靠性进行分析.分析结果表明,该模型能够从运动员成绩数据中找到其将来变化态势,提高了运动员成绩的预测精度,预测结果具有一定的应用价值.
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