We propose a 3D point cloud semantic segmentation algorithm based on density awareness and self-attention mechanism to address the issue of insufficient utilization of inter point density information and spatial location features in existing 3D point cloud semantic segmentation algorithms. First, based on the adaptive K-Nearest Neighbor (KNN) algorithm and local density position encoding, a density awareness convolutional module is constructed to effectively extract key density information between points, enhance the depth of information expression of initial input features, and enhance the algorithm's ability to capture local features. Then, a spatial feature self-attention module is constructed to enhance the correlation between global contextual information and spatial location information based on self-attention and spatial-attention mechanisms. The global and local features are effectively aggregated to extract deeper contextual features, enhancing the segmentation performance of the algorithm. Finally, extensive experiments are conducted on the public S3DIS dataset and ScanNet dataset. The experimental results show that the mean intersection over union of our algorithm reaches 69.11% and 72.52%, respectively, shows significant improvement compared with other algorithms, verifying the proposed algorithm has good segmentation and generalization performances.