With the development of digital technology, various fields generate and share a large amount of visual content. Image retrieval is a hot research direction in the field of computer vision. Efficient and accurate retrieval of query content from massive data is the ultimate form pursued by image retrieval technology. In recent years, the rise of deep learning technology has promoted the rapid development of the field of computer vision. Due to the powerful expressive ability of deep features on image content, image retrieval based on deep learning has become the most cutting-edge research direction in CBIR technology. This paper summarizes the relevant research on the classic deep learning image retrieval technology in recent years, first introduces the form of the CBIR problem, and then lists the classic datasets in this field. Afterwards, content-based deep image retrieval methods are reviewed from the perspectives of network models, deep feature extraction, and retrieval types. Finally, summarize the problems to be solved urgently in the current research, and look forward to the future research direction.