In view of the problem that the traditional methods can not be directly effective on such data clustering analysis, a clustering method of multivariate time series data based on component attributes affinity propagation is proposed. The overall distance between multivariate time series data can be measured by dynamic time warping. The clustering analysis of the overall distance matrix and component approximate distance matrix is processed by using the affinity propagation clustering algorithm, considering the relationship between two sequence data from the two kinds of perspectives. The synthetical relationship matrix of the raw multivariate time series data is used for clustering by using the affinity propagation method. The numerical experiment results how that, compared with the traditional clustering methods, the proposed method not only can effectively reflect the relationship of the overall data characteristics, but also improve the clustering effect of the original time series data through the analysis of the relationship between the important component attributes.