Department of Physical Education, Sanquan College of Xinxiang Medical University, Xinxiang 453003, China.
Comput Intell Neurosci. 2022 May 27;2022:3621316. doi: 10.1155/2022/3621316. eCollection 2022.
This paper proposes a network model recurrent fully connected network (RFC-Net) based on recurrent full convolution and polarization change. RFC-Net enriches the network by reconstructing and fine-tuning the fully convolutional network and adding recurrent convolutions to it. By studying the data mining technology of multidimensional association rules, based on the existing algorithms, this paper improves the shortcomings of the algorithms and realizes an efficient and practical method for data mining based on interdimensional multidimensional association rules. On the basis of mastering the actual student information, the effectiveness of the method is tested, and an employment analysis system based on association rules is established. Aiming at the fact that traditional grade prediction methods ignore the different influences of different behavioral characteristics on grades, and considering that behavioral data in different periods have different influences on student grades, the grade prediction problem is abstracted into a time series classification problem. The mechanism is combined with long short-term memory neural network to construct a performance prediction model based on Attention-BiLSTM. Experiments show that the prediction model proposed in this paper improves the accuracy and effectively improves the prediction quality compared with the logistic regression model with a better prediction effect in the traditional benchmark model and the long short-term memory neural network model without the introduction of the attention mechanism. Research shows that physical performance and academic performance are not contradictory. We must face up to the status of physical exercise in schools; as long as physical exercise is properly arranged, it can inspire students to form a spirit of unity, interaction, positivity, and perseverance in cultural studies.
本文提出了一种基于递归全卷积和极化变换的网络模型递归全连接网络（RFC-Net）。RFC-Net 通过对全卷积网络进行重构和微调，并向其中添加递归卷积，从而丰富了网络。通过研究多维关联规则的数据挖掘技术，在现有算法的基础上，本文改进了算法的缺点，实现了一种基于多维关联规则的数据挖掘的高效实用方法。在掌握实际学生信息的基础上，测试了该方法的有效性，并建立了基于关联规则的就业分析系统。针对传统成绩预测方法忽略了不同行为特征对成绩的不同影响，以及考虑到不同时期的行为数据对学生成绩的影响不同，将成绩预测问题抽象为时间序列分类问题。将机制与长短时记忆神经网络相结合，构建了基于注意力-BiLSTM 的性能预测模型。实验表明，与传统基准模型中的逻辑回归模型和未引入注意力机制的长短时记忆神经网络模型相比，本文提出的预测模型提高了准确性，有效地提高了预测质量。研究表明，身体素质和学习成绩并不矛盾。我们必须正视学校体育锻炼的现状；只要合理安排体育锻炼，就可以激发学生在文化学习中形成团结、互动、积极和毅力的精神。
