Semantic segmentation of point cloud data plays an important role for 3D scene understanding and reconstruction, autonomous driving, and robot navigation. In this study, DGPoint, a dynamic graph convolution network based on the PointNet++ architecture, is proposed to address the insufficient segmentation accuracy due to insufficient local feature extraction of point clouds by existing methods. First, the feature aggregation function in edge convolution compensates for loss using a dual-channel pooling operation, which can better retain the fine-grained local characteristics of the point cloud. Then, to accomplish the impact of dynamic graph updates, K-nearest neighbors algorithm is used to determine new local regions prior to edge convolution. Additionally, to ensure the accuracy of edge feature extraction, the designed encoder is repeated multiple times, and the extracted features are concatenated in a jump-connection style before being input to the decoder. Experimental results of the S3DIS data set show that DGPoint effectively solves the shortcomings of the insufficient local feature extraction and improves semantic segmentation accuracy with the mean intersection over union of 68.3% and overall accuracy of 86.2% compared with other methods.