Intravascular optical coherence tomography (IVOCT) is a minimally invasive imaging model that currently has the highest resolution. It is capable of providing information of the vascular lumen morphology and near-microscopic structures of the vessel wall. For each pullback of the target vessel, hundreds or thousands of B-scan images are obtained in routine clinical applications. Manual image analysis is time-consuming and laborious, and the findings depend on the operators' professional ability in some sense. Recently, as deep learning technology has continuously made significant breakthroughs in the medical imaging field, it has also been used in the computer-aided automated analysis of IVOCT images. This study outlines the applications of deep learning in IVOCT, primarily involving image segmentation, tissue characterization, plaque classification, and object detection. The benefits and limitations of the existing approaches are discussed, and the future possible development is described.