The work aims to propose an improved SMOTE oversampling method to deal with the minority class low data registration recognition accuracy of printing mark images caused by the imbalanced training dataset so as to improve the recognition accuracy of data. The texture features were extracted from the gray-level run-length matrix (GLRLM) of the printing mark images to form multi-dimensional feature data as the input vectors of the model. The oversampling parameter of the minority class was computed based on the neighborhood information of the minority class. Different oversampling strategies were implemented for the minority class. An unbalanced training dataset was learned to construct a support vector machine (SVM) model to realize the printing registration status recognition.The experimental results showed that, in terms of different imbalanced printing datasets, the method proposed in this paper can obtain the values of three evaluation indexes, geometric mean of average classification accuracy Gmean=0.8507, recall rate Re=0.7192 and area under the curve A=0.8549.The proposed method outperforms the SMOTE, the IS and the SVM in the experiment in classifying the different imbalanced printing registration datasets.