For most multivariate time series segmentation algorithms, the selection of segmentation points and the determination of the number of segments often need to be completed independently, which greatly increase the computational complexity of the algorithm. In order to solve the above problems, an adaptive greedy Gaussian segmentation algorithm based on multivariate time series is proposed in this paper. The algorithm interprets the data on the segmentation of multivariate time series as independent samples from Gaussian distribution, and the segmentation problem is transformed into the parameter solution problem of Gaussian segmentation model. In order to improve the learning efficiency, the greedy search method is adopted to maximize the likelihood value of each segment to find the optimal segment point approximately. During the searching process, the information gain method is adopted to adaptively obtain the optimal number of segments, which avoids from realizing the determination of the number of segments and the selection of segment points independently to reduce the computational complexity. Experimental results show that our algorithm has been effectively verified on different data sets .