The current printing registration recognition methods usually use the feature extraction of experienced manual design. To solve this problem, a convolutional neural network model without manual image feature collection is proposed to realize printing registration recognition. The image enhancement technology is used to equalize the imbalanced training set to increase the amount of training set and improve the recognition accuracy of the model. The structural parameters of the printing registration recognition model based on AlexNet network are designed, and the effects of batch sample number and basic learning rate on the model performance are analyzed. The proposed method achieves promising experimental results. The total accuracy of printing registration recognition is 0.9860 with the recall of 1.0000 and the geometric means of classification accuracy of 0.9869. The method in this paper automatically extracts image features, and does not rely on artificially designed feature extraction methods. On the constructed data set, the classification performance of the proposed method is superior to the experimental support vector machine method.