The work aims to study the printing registration recognition method based on the constrained spectral clustering algorithm, in order to overcome the problem of high cost of obtaining the label of the printing mark images in actual production.The constraints of the must-link and the connot-link were set and extended based on a few of labeled samples. The similarity matrix of the samples was computed in the Euclidean space and then revised by the extended constraint relationship, to construct the eigenvector space of the samples. The K-means method divided the samples in the eigenvector space into two sections, including the samples with the registration label and the ones with the misregistraion label. The proposed method was performed the experimental data, with the best printing registration recognition accuracy of 98.11%. The proposed method with 30 pairs of constraints outperformed the unsupervised spectral clustering algorithm, the Naivebayes method and the decision tree method in terms of the recognition accuracy. The recognition accuracy of the proposed method was nearly equal to the one of the support vector machine (SVM) method. Besides, compared with the SVM method, the proposed method was lower in the cost of obtaining the labels of the printing mark images and in the time-consumption of model construction and recognition.The proposed method achieves the higher accuracy of printing registration with the low cost of acquiring the labels of printing mark images.