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• For CFD data, a local key time steps selection method based on DML is proposed. • Compared with one of the existing methods, ours yields better selection results. • The good generalization of the proposed method is demonstrated. As one of the main technologies of flow visualization, key time steps selection plays a key role in solving storage limit and has been intensively studied. In this paper, we introduce Deep Metric Learning (DML) into key time steps selection for Computational Fluid Dynamics (CFD) data and propose a local selection method based on DML. In specific, the proposed method samples small patches from CFD data, trains a Siamese deep neural network which has a symmetry structure with two Convolutional Neural Networks (CNN), and then selects the key time steps according to the similarities between consecutive time steps which are assessed by the networks. Compared with one of the existing local selection methods, the Myers's method, our method has advantages in accuracy, precision and recall, and the selection results are better. Experimental results also demonstrate the good generalization of the proposed method on CFD datasets. [ABSTRACT FROM AUTHOR]