Shenyang Sport University, Shenyang 110102, China.
Handan University, Handan, 056005, China.
Comput Math Methods Med. 2022 Mar 25;2022:7295833. doi: 10.1155/2022/7295833. eCollection 2022.
In order to improve the prediction effect of sports training performance and improve the effect of sports training, this paper classifies the sports training image area, refines the image into different areas, finds suspicious areas, and completes the error prediction. Moreover, this paper calculates the regional similarity of sports training images in the fully connected layer of the convolutional neural network and introduces the local linear weighting method for analysis. In addition, this paper gives a certain weight to each prediction point near the area to be predicted and selects the suspicious area feature based on the multievaluation standard fusion method. Finally, this paper combines the convolutional neural network algorithm to construct a sports training performance prediction system to improve the effect of sports training and design experiments to verify the system proposed in this paper. From the experimental research results, we can see that the sports training performance prediction system based on the convolutional neural network proposed in this paper has good practical effects.
为了提高体育训练性能的预测效果，提升体育训练的效果，本文对体育训练图像区域进行分类，将图像细化为不同区域，寻找可疑区域，并完成错误预测。此外，本文在卷积神经网络的全连接层中计算体育训练图像的区域相似性，并引入局部线性加权方法进行分析。另外，本文给预测区域附近的每个预测点赋予一定的权重，并基于多评价标准融合方法选择可疑区域特征。最后，本文结合卷积神经网络算法构建体育训练性能预测系统，以提高体育训练的效果，并设计实验验证本文提出的系统。从实验研究结果可以看出，本文提出的基于卷积神经网络的体育训练性能预测系统具有良好的实际效果。
