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Time series events clustering is the basis of studying the classification of events and mining analysis. Most of the existing clustering methods directly aim at continuous events with time attribute and complex structure, but the transformation of clustering objects is not considered, hence the accuracy of clustering is extremely low, and the efficiency is limited. In response to these problems, a time series events oriented dynamic matrix clustering method RDMC is proposed. Firstly, the r-nearest neighbor evaluation system is established to measure the representativeness of the event according to the evaluation value, and the candidate set of RDS (representative and diversifying sequences) is constructed by the backward difference calculation strategy of the nearest neighbor score. Secondly, a method of RDS selection based on combinatorial optimization is proposed to obtain the optimal solution of RDS from the candidate set quickly. Finally, on the basis of dynamically constructing the distance matrix between RDS and the data set, a matrix clustering method based on K-means is proposed to realize the effective division of time series events. Experimental results show that compared with the existing methods, the method proposed in this paper has obvious advantages in clustering accuracy, clustering reliability, and clustering efficiency.