The goal of transfer learning is to solve the problem of insufficient training samples in the target domain.It can transfer the acquired knowledge from related source domain to the target domain.It relaxes two basic assumptions in traditional machine learning:the training samples and the new test samples satisfy the conditions of independent and identical distribution; furthermore,there must be enough training samples to learn a good classification model.According to whether the feature space of the source domain and the target domain are the same,it can be divided into homogeneous transfer learning and heterogeneous transfer learning.This paper mainly reviews the related research progress of homogeneous transfer learning,introduces the theory,algorithm and application of homogeneous transfer learning,and points out the hotissues of homogeneous transfer learning.