Shapelet is the most identifiable subsequence in time series， which has been extensively studied by researchers from various fields since it was proposed. In this process， many effective shapelet discovery techniques have been proposed for time series classification. However， candidate shapelets of multivariate time series may have different lengths and different sources of variables， making it difficult to directly compare them， which presents a unique challenge to the classification method of multivariable time series based on shapelet. we propose Multi-shapelet， a multivariate time series classification method based on unsupervised representation learning and shapelets. Firstly， Multi-shapelet uses a hybrid model DC-GNN （Dilated convolution neural network and graph neural network） as an encoder to embed candidate shapelets of different lengths into a unified shapelet selection space for comparison between shapelets. Secondly， a new loss function is proposed to train the encoder in an unsupervised learning manner， so that after DC-GNN encodes the shapelet to obtain the corresponding embedding， the topology and the original space formed by the relative positions between the embeddings corresponding to the shapelet belonging to the same class. The relationship between the topologies formed by the relative positions of the shapelet in the middle is closer to a proportional reduction， which is very important for the subsequent similarity-based pruning process. Finally， the K-means clustering and simulated annealing algorithm are proposed to prune and select shapelets to select a set of shapelets with strong classification ability. Experimental results on 18 UEA multivariable time series datasets show that the overall accuracy of Multi-shapelet is significantly better than other methods.