Abstract: In response to the limitation of existing point cloud classification and segmentation methods that use max pooling to aggregate local neighborhood features, which leads to the loss of important information beyond the maximum value, this paper proposes a point cloud classification and segmentation network that combines Adaptive Graph Convolution (AGConv) and Attention Pooling (AP). Firstly, a local graph structure of the point cloud is constructed using K-nearest neighbors algorithm, and adaptive convolution kernels are generated based on the features of the points, enabling flexible and accurate capturing of local neighborhood features. Secondly, to effectively enhance feature aggregation, attention pooling is utilized to define an energy function and obtain weight values, which are used to weight and aggregate more representative local features of the point cloud. Finally, adaptive graph convolution and attention pooling are stacked to extract global features layer by layer, thereby improving the accuracy of classification and segmentation. Experimental results demonstrate that compared with the benchmark network, the average class accuracy of point cloud classification is improved by 0.9%, and the average intersection over union of part segmentation and semantic segmentation is improved by 0.8% and 0.3% respectively. This demonstrates that the algorithm can effectively improve the accuracy of point cloud classification and segmentation, and has high robustness.