As the current algorithms fail to fully extract local features and result in significant degradation of network accuracy when performing geometric transformations such as translation， scaling， and rotation on point cloud data， this paper proposes a dynamic graph attention-based 3D point cloud recognition and segmentation algorithm based on adaptive generated convolutional kernels. Firstly， the positional information of the center point in the receptive field is used to enhance the contextual information perception of neighboring points. The receptive field is reconstructed to enable sufficient interaction of feature information within the receptive field and enhance the contextual information by improving the self-attention mechanism. Then， an adaptive generated convolutional kernel is constructed to capture changing point cloud topology information and adaptively generate convolutional kernel weights to enhance network performance. Finally， a dynamic graph attention convolutional operator is built， and a dynamic network for point cloud recognition and a U-shaped network for segmentation are designed. The experimental results show that the proposed algorithm achieves a recognition accuracy of 94.0% in the ModelNet40 point cloud recognition dataset， and the instance mean intersection over union reaches 86.2% in the ShapeNet Part point cloud semantic segmentation dataset. The algorithm proposed can extract critical feature information from 3D point clouds and is capable of 3D point cloud recognition and segmentation.