Currently, most point cloud semantic segmentation methods based on graph convolution overlook the critical aspect of edge construction, resulting in an incomplete representation of the features of local regions. To address this limitation, we propose a novel graph convolutional network AE-GCN that integrates edge enhancement with an attention mechanism. First, we incorporate neighboring point features into the edges rather than solely considering feature differences between the central point and its neighboring points. Second, introducing an attention mechanism ensures a more comprehensive utilization of local information within the point cloud. Finally, we employ a U-Shape segmentation structure to improve the network's semantic point cloud segmentation adaptability. Our experiments on two public datasets, Toronto_3D and S3DIS, demonstrate that AE-GCN outperforms most current methods. Specifically, on the Toronto_3D dataset, AE-GCN achieves a competitive average intersection-to-union ratio of 80.3% and an overall accuracy of 97.1%. Furthermore, on the S3DIS dataset, the model attains an average intersection-to-union ratio of 68.0% and an overall accuracy of 87.2%.