In response to the deficiencies in local context information extraction and neighboring point feature expression in deep learning-based point cloud classification and segmentation networks， as well as the problem of maxpooling leading to the loss of suboptimal information， a point cloud classification and segmentation algorithm that combines dual attention and weighted dynamic graph convolutional networks was proposed. Firstly， the weighted dynamic graph convolution used a weighted k-nearest neighbor algorithm to construct a robust local structure and introduced an enhanced edge convolution module to apply weights to point features， thereby obtaining enhanced edge features. Then， channel attention was used to construct channel correlations and unleash the potential of each channel， followed by spatial attention to perceive the spatial structure of 3D point clouds， enhancing the expression of local semantic features and extracting effective contextual and deep semantic information. Finally， TopK pooling was employed to add suboptimal features. Experimental results show that the algorithm achieves an overall classification accuracy of 93.36% on the ModelNet40 classification dataset and an average intersection over union of 85.96% on the ShapeNet Part segmentation dataset， effectively extracting contextual information and enhanced neighboring point feature expression， demonstrating the effectiveness of the algorithm.