Learning effective feature representations and similarity measures are crucial to the retrieval performance of a content-based image retrieval system. In the past, the system works on the low-level visual features of input query image, which does not give satisfactory retrieval results, so, despite extensive research efforts for decades, it remains one of the most challenging problem in computer vision field. The main problem is the well-known "semantic gap", which exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human. In the past, the content-based image retrieval system only works on the low-level visual features, which cannot solve "semantic gap" issue. Recently, the fast development of deep learning brings hope for the issue. Deep learning roots from the research of artificial neural network. In order to form more abstract high-level, deep learning combines low-level features, finds the regularities of distribution, which is different from other algorithm. Inspired by recent successes of deep learning techniques for computer vision, speech recognition, natural language process, image and video analysis, multimedia, in this paper, we apply deep learning to solve the "semantic gap" issue in content-based image retrieval.