The recurrent convolutional neural network is an advanced neural network that integrates deep structure and convolution calculation. The feedforward neural network with convolution operation and deep structure is an important method of deep learning. In this paper, the convolutional neural network and the recurrent neural network are combined to establish a recurrent convolutional neural network model composed of anomalies, LSTM (Long Short-Term Memory), and CNN. This study combines the principal component analysis method to predict and analyze the test results of students’ physical fitness standards. The innovation lies in the introduction of the function of the recurrent convolutional network and the use of principal component analysis to conduct qualitative research on seven evaluation indicators that reflect the three aspects of students’ physical health. The results of the study clearly show that there is a strong correlation between some indicators, such as standing long jump and sitting bends which may have a strong correlation. The first principal component eigenvalue has the highest contribution rate, which mainly reflects the five indicators of standing long jump, sitting forward bend, pull-up, 50 m sprint, and 1000 m long-distance running. This shows that the physical fitness indicators have a great impact on the physical health of students, which also reflects the current status of students’ physical fitness problems. The results of principal component analysis are scientific and reasonable.

中文翻译：

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基于循环卷积神经网络的学生体质健康标准测试结果的主成分分析与预测

循环卷积神经网络是一种集深度结构和卷积计算于一体的高级神经网络。具有卷积运算和深度结构的前馈神经网络是深度学习的重要方法。本文将卷积神经网络和循环神经网络结合起来，建立了一个由异常、LSTM（Long Short-Term Memory）和CNN组成的循环卷积神经网络模型。本研究结合主成分分析法对学生体质标准的测试结果进行预测分析。创新之处在于引入循环卷积网络的功能，利用主成分分析，对反映学生身体健康三个方面的七个评价指标进行定性研究。研究结果清楚地表明，一些指标之间存在很强的相关性，例如站立跳远和坐姿弯曲可能具有很强的相关性。第一主成分特征值贡献率最高，主要反映立定跳远、坐姿前弯、引体向上、50米冲刺、1000米长跑5个指标。由此可见，体质指标对学生的身体健康有很大的影响，也反映了学生体质问题的现状。