In recent years, China’s sports industry has achieved good development, but the efficiency of athletes in the training process is difficult to have scientific guarantee. How to use scientific algorithm and data mining technology to accurately guide the sports training process has become a hot spot. Based on this, this paper studies the gait recognition model of sports training based on convolutional neural network algorithm. First, this paper analyzes the research status of gait recognition in the process of training and optimizes and improves the deficiencies in sports training. Then, the convolutional neural network algorithm and data mining technology are optimized and analyzed in the gait recognition model. Finally, the experimental results show that the convolutional neural network algorithm can realize the recognition and model reconstruction of athletes’ gait in the training process and can make the optimal strategy according to the gait differences of different athletes in the training process, and the recognition accuracy of athletes’ gait can reach more than 97%.

中文翻译：

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基于卷积神经网络和数据挖掘的运动训练系统

近年来，我国体育产业取得了良好的发展，但运动员在训练过程中的效率却很难有科学保障。如何利用科学算法和数据挖掘技术精准指导运动训练过程已成为热点。基于此，本文研究基于卷积神经网络算法的运动训练步态识别模型。首先，分析了训练过程中步态识别的研究现状，并对运动训练中的不足进行了优化和改进。然后，对步态识别模型中的卷积神经网络算法和数据挖掘技术进行优化和分析。最后实验结果表明，卷积神经网络算法能够实现运动员训练过程中步态的识别和模型重建，并能根据不同运动员训练过程中的步态差异制定最优策略，识别准确率较高。运动员步态改善率可达97%以上。