Aiming at the inaccuracy of traditional abnormal flow detection model in the industrial control system, an abnormal flow detection model based on Convolutional Neural Network (CNN) was proposed. The proposed model was based on CNN algorithm and consisted of a convolutional layer, a full connection layer, a dropout layer and an output layer. Firstly, the actual collected network flow characteristic values were scaled to a range corresponding to the grayscale pixel values, and the network flow grayscale map was generated. Secondly, the generated network traffic grayscale image was put into the designed convolutional neural network structure for training and model tuning. Finally, the trained model was used to the abnormal flow detection of the industrial control network. The experimental results show that the proposed model has a recognition accuracy of 97.88%, which is 5 percentage points higher than that of Back Propagation (BP) neural network with the existing highest accuracy.