Deep learning-based point cloud semantic segmentation models often adopt complex attention mechanisms for improvement but show deficiencies in extracting local deep semantic features and neighbor point feature expressions. Therefore, this paper proposes a point cloud semantic segmentation model that combines the weighted Knearest neighbors algorithm with convolutional block attention. On the architecture of the Dynamic Graph Convolutional Neural Network, a weighted K-nearest neighbors algorithm is designed to obtain more effective local neighborhoods; then, convolutional attention is introduced to process features within the local neighborhoods. In the convolutional attention, channel attention is used to enhance the correlation among point cloud channels, and spatial attention is applied to perceive the three-dimensional spatial structure, acquiring contextual information and deep semantic features. Experimental results show that the model achieves an average Intersection over Union (IoU) of 89.92% on the ShapeNet Part dataset and 61.2% on the S3DIS indoor semantic segmentation dataset, demonstrating higher segmentation accuracy compared to other methods.