The classical dynamic programming based segmentation algorithm is only suitable for low dimensional time series. To solve this problem, a segmentation method of multivariate time series with factor model and dynamic programming is proposed. Firstly, incremental clustering is used to automatically cluster variable sequences with similar trend. Then, a dynamic factor model is introduced to make the low-dimension multivariate time series obtained after dimension reduction reflect the overall trend of the original multivariate time series. Finally, the segmentation of high-dimension multivariate time series in the framework of low-dimension time series is realized by using dynamic programming. The experimental studies show that the proposed method has a good segmentation effect on multivariate time series data with a large number of variables.