The graph-based multi-view methods have achieved state-of-the-art results in 3D shape analysis tasks by taking advantage of graph convolutional networks (GCN) to process discrete data. However, the homogeneity of the traditional GCN aggregation operator leads to a problem in aggregating neighborhood information, i.e., if several views have the same neighbors, the same node embeddings will be generated, resulting in feature redundancy. To address this problem, we propose a Multi-view Path Aggregation Graph Network (MVPNet) for 3D shape analysis, which aims to extract a particular path from a graph composed of multiple views and aggregate it into an effective 3D shape descriptor. Specifically, we first extract a path in the graph through dynamic walking, and update the path status while searching for new nodes during the walking. Then we embed the position information of the nodes in the order of the nodes in the path. Finally, we propose to aggregate the features of a path employing a Path Transformer that is capable of handling ordered sequences. A path contains richer semantic and structural information than a traditional subgraph. To demonstrate the effectiveness of our proposed method, we conduct extensive experiments on three benchmark datasets, namely ModelNet, ShapeNetCore55 and MCB, and these experiments prove that the method outperforms the current methods in 3D shape classification and retrieval tasks.