How to effectively identify the intrusion attack behavior of the Industrial Internet of Things (IIOT) is a new challenge. Aiming at the problems of low intrusion detection feature extraction, low detection efficiency, and poor adaptability in IIOT, an intelligent intrusion detection method based on deep learning is proposed. First, improve the sampling algorithm in data processing for adjusting the number of samples in a few categories to improve the detection accuracy. Second, build a stacked denoising convolutional self-encoding network to extract key features. Combine the convolutional neural network and the denoising self-encoder to enhance feature recognition ability. In order to avoid information loss and information ambiguity, improve the pooling operation to increase its adaptive processing ability, and use Adam algorithm to obtain the optimal parameters during model training. Finally, use the NSL-KDD dataset to test the performance of the proposed method. Experimental results show that the accuracy of the method is 3.66%, 4.93%, and 0.04% higher than the existing RNN, DBN, and IDMBCNN, respectively. Compared with the SDCAENN test without sampling algorithm, the detection accuracy of U2R and R2L is improved by 17.57 % and 3.28%.