As an important research direction in the field of machine learning, deep learning technology has been infiltrated into various fields of scientific research due to its powerful feature of learning ability and excellent portability. In particular, the achievements in the frontier fields of deep learning have played a positive role in the recognition, classification, segmentation and quantification of medical images. Optical coherence tomography (OCT) technology has the characteristics of non-invasive, high resolution, non-invasive acquisition of micron-level high resolution 3D sectional image of biological tissue, and has been widely used in ophthalmology, vascular, dermatology and other biomedical fields. This paper first introduces the basic principles of OCT and deep learning and the advantages of their combination in the field of skin, and then elaborates the application of deep learning in the field of skin OCT medical images, including the analysis and classification of skin cancer, beauty, fingertip image segmentation, and the qualitative and quantitative evaluation of damaged skin. Key words: optical coherence tomography; medical imaging; deep learning; skin disease; convolutional neural network