We propose to tackle dynamic texture video classification as a pattern mining problem. In a nutshell, videos are represented by frequent sequences of representative patches. Firstly, we use a Gaussian Mixture Model to make the clustering of patches from training videos. Secondly, a soft assignment is used as an encoding method to construct sequences of probability vectors (p-sequences) representing sequences of spatio-temporal patches. Thirdly, for each class, we mine meaningful motifs appearing inside the training p-sequences by means of an adapted data mining approach. Finally, feature vectors are constructed from the mined motifs, using the probabilistic support, which quantifies the match between the p-sequences, of the video to be classified, and the key-motifs of the classes. Experimental results and analysis for dynamic texture classification on benchmark datasets (i.e. UCLA, Traffic) show the interest of the proposed method.