The work aims to study a preprocessing method for the imbalanced printing image training set in view of the problem of poor recognition accuracy of unaligned printing images in the detection of imbalanced printing image registration. An integrated sampling preprocessing method for the imbalanced printing image training set was proposed. Firstly, the imbalanced training set was divided into support vectors and non-support vectors through the support vector machine model. Secondly, in order to balance the training set, the support vectors in the minority class (i.e., the unaligned printing images) were oversampled and the non-support vectors in the majority class (i.e., the aligned printing images) were undersampled. Finally, the pre-processed balanced training set was used to train the support vector machine model, and the model parameters were optimized. The proposed integrated sampling method was used to pre-process the imbalanced training set to obtain the support vector machine model. The recognition rate of the minority class a+ obtained through the recognition of printing image registration was 0.9375, the geometric mean Gmean of the recognition accuracy was 0.9437 and the F-score was 0.9574. The proposed method outperforms other methods in the experiment in terms of the recognition accuracy a+ of the unaligned printing images, Gmean and F-score.