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The classification and part segmentation of point cloud models are the basic tasks of 3D point cloud data processing, and the core is to obtain point cloud features that can effectively represent 3D models. This paper proposes a 3D point cloud feature learning network that introduces attention mechanisms. The network adopts a hierarchical point cloud feature extraction method. In the process of hierarchical feature extraction, the feature channel attention mechanism is adopted to obtain the correlation among channels, and the key channel information is enhanced. The spatial position attention mechanism is adopted to obtain the attention weight of each point based on the spatial information of the points. The enhanced point cloud feature is obtained by combining two or more attention mechanisms. Based on this feature, multi-level feature extraction is performed to obtain the final point cloud features for downstream tasks. Shape classification and part segmentation experiments are performed on Model- Net40 and ShapeNet datasets, respectively. The experimental results show that the proposed method can achieve high-precision and robust 3D point cloud shape classification and segmentation. [ABSTRACT FROM AUTHOR]