The explosive development of deep learning technology has led another wave of machine learning in recent years. Deep neural network, with the ability to recognize and extract abstract features, fit nonlinear relationships, against interference factors and generalization, is widely used in autopilot, target recognition, machine translation, speech recognition and other fields. The convolutional neural networks (CNN) are popular in optical information processing. In this paper, we introduce the basic concepts and structural components of CNN in detail, and review the applications in digital holography, fringe patterns analysis, phase unwrapping, ghost imaging, Fourier ptychographic microscopy, super-resolution microscopy, scattering medium imaging, optical tomography imaging, etc. We summarize the typical applications and existing shortages of CNN in optical information processing, and finally prospect the future development of convolutional neural networks.