With the booming development of point cloud-based applications such as intelligent driving and robot navigation, semantic segmentation of point clouds has gradually become a hotspot of research. However, the existing methods for semantic segmentation of point clouds suffer from the shortcomings of insufficient local feature extraction and incomplete feature fusion. To address these shortcomings, we propose corresponding solutions. For the phenomenon of insufficient local feature extraction, the explicit features of neighboring points are associated by embedding the coordinates, directions, distances and other relevant information of the neighboring points. For the phenomenon of incomplete feature fusion, a hybrid pooling method combining maximum pooling and self-attention pooling is proposed. The network architecture in this paper is based on PointNet++ and is combined with the proposed local feature extraction and fusion method. The experimental results on the S3DIS dataset show that the evaluation indices have been improved to different degrees compared with baseline PointNet++ method, which confirms the effectiveness and superiority of new method.