摘要： 在工业分类预测中，有标签数据稀缺且标记成本高，导致模型预测不准确，同时大多数无标签数据中的特征未得到合理利用，模型的泛化能力不足。为了解决这个问题，该研究将有标签数据和无标签数据通过有监督学习和无监督学习相结合，提升模型预测准确率。该模型首先在深度自编码通道上，分别添加高斯噪声和稀疏性约束，提取与分类相关更具代表性的特征表示；其次在编码器与解码器之间引入横向连接，过滤与分类任务不相关的信息，使得网络能够更好地学习关键变量的特征表示，并在网络顶层添加有监督学习路径来实现分类识别；然后添加原始编码器，与解码器中对应隐含层的输出一起训练，从而实现无监督学习路径，有效利用无标签数据中信息；最后通过有监督损失与无监督损失函数构造总损失函数，实现对工业生产中关键变量进行分类预测。实验结果表明，与常用的有监督学习模型和传统的半监督学习模型相比，该算法的分类预测准确率得到了有效提高，并且精确度、召回度和F1分数均表现出改进。

Abstract: In industrial classification prediction, the labeled data are scarce and the labeling cost is high, which leads to the inaccurate prediction of the model. At the same time, the features in most unlabeled data are not rationally used, and the generalization ability of the model is insufficient. In order to solve this problem, this study combines labeled data and unlabeled data through supervised learning and unsupervised learning to improve the prediction accuracy of the model. Firstly, Gaussian noise and sparsity constraints were added to the deep autoencoder channel to extract more representative feature representations related to classification. Secondly, a lateral connection is introduced between the encoder and the decoder to filter the information irrelevant to the classification task, so that the network can better learn the feature representations of key variables, and a supervised learning path is added to the top layer of the network to realize classification and recognition. Then, the original encoder is added and trained together with the output of the corresponding hidden layer in the decoder, so as to realize the unsupervised learning path and effectively use the information in the unlabeled data. Finally, the total loss function was constructed by the supervised loss function and the unsupervised loss function to realize the classification and prediction of key variables in industrial production. Experimental results show that compared with the commonly used supervised learning model and the traditional semi-supervised learning model, the classification prediction accuracy of the proposed algorithm is effectively improved, and the precision, recall and F1 score are improved.