The traditional Gath-Geva (G-G) fuzzy segmentation algorithm needs to set parameters and has low segmentation efficiency for high-dimensional time series. To address such matters, a fuzzy segmentation method for multivariate time series based on kernel principal component analysis(KPCA) and G-G clustering is proposed. Firstly, the KPCA is used to extract key features of multivariate time series to remove the impacts of redundant and irrelevant variables. Then, the upper bound for the number of segments is determined using the affinity propagation(AP). Finally, taking the time information into account, the segmentation of high-dimension multivariate time series in the framework of low-dimension time series is realized with the modified Davies-Bouldin index(MDBI) and G-G fuzzy clustering. The experimental results show that the proposed algorithm can detect some sudden and gradual change trend of time series quickly and effectively, which improves the accuracy and operation efficiency.