Deep learning allows software to assist in diagnosis to be developed and applied more aggressively and efficiently, whereas the color variability of histopathology images degrades the performance of these algorithms. Stain normalization can address image heterogeneity arising from scanner effects, different staining methods, patient’s disease states, staining times, and other factors. Virtual staining can eliminate slide staining and reduce slide preparation steps, reducing sample preparation time for the clinic and saving significant costs. In the absence of annotated training data, pathology image data augmentation is performed by creating artificial samples with realistic texture, color and style to facilitate network training. In this paper, we made a review on staining processing of histological pathology images in deep learning pathology analysis to provide a reference for histological pathology maps in clinical applications and research.