To address the limitations of existing point-based networks, which treat all points with equal emphasis, thereby overlooking crucial features, this paper introduces an attention mechanism to lidar point cloud processing. This mechanism, referred to as the CSA module, integrates the channel attention and spatial attention elements. In a data-driven approach, the two proposed modules autonomously learn the importance of different feature channel information and spatial location information, thereby enhancing the performance of the network on point cloud classification and segmentation tasks. This paper introduces the two modules stated above in a point-based network and proposes a CSA-PointNet++ architecture. The results reveal that the proposed method achieves an accuracy of 93.20% for classification experiments on the ModelNet40 dataset and a mean intersection over union (mIoU) of 82.62% for part segmentation experiments on the ShapeNetPart dataset. This performance is better than that of other comparative methods, indicating the effectiveness of the proposed network. Moreover, classification experiments of the proposed method on a real-world self-constructed dataset yield an accuracy of 92.14%, demonstrating the excellent generalization capability of the proposed network on real-world data.