Scattering media will change the propagation direction and path of photons, resulting in the degradation of image quality and even the formation of speckle. Theoretically, information of targets can be recovered by using the transmission matrix of the scattering medium, but the process to solve the transmission matrix is too complicated. The rapid development of deep learning provides a new method to solve the problem of imaging through scattering media. As a typical method for solving inverse problems, deep learning can recover the target information accurately, improve the imaging quality and so on, and it has achieved many important research results in the field of scattering imaging. The existing deep learning methods could be divided into two categories: supervised learning and unsupervised learning. Herein, we summarize the research progress of imaging through scattering media based on deep learning from these two aspects, and compare the performance of some intelligentalgorithmic imaging techniques in terms of network structure, imaging quality and generalization of deep learning. Finally, the advantages and challenges of deep learning-based imaging technology are analyzed, and the future development of this field is prospected.