Microscopic imaging providing the real-space information of matter, plays an important role for understanding the correlations between structure and properties in the field of materials science. For the microscopic images of different kinds of objects at different scales, it is a time-consuming task to retrieve useful information on morphology, size, distribution, intensity etc. Alternatively, deep learning has shown great potential in the applications on complicated systems for its ability of extracting useful information automatically. Recently, researchers have utilized deep learning methods on imaging analysis to identify structures and retrieve the linkage between microstructure and performance. In this review, we summarize the recent progresses of the applications of deep learning analysis on microscopic imaging, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and scanning probe microscopy (SPM). We present sequentially the basic concepts of deep learning methods, the review of the applications on imaging analysis, and our perspective on the future development. Based on the published results, a general workflow of deep learning analysis is put forward. © 2020