针对现有三维点云语义分割算法对点间密度信息以及空间位置特征利用不充分的问题，提出一种基于密度感知和自注意力机制的三维点云语义分割算法。首先，基于自适应K近邻（KNN）算法和局部密度位置编码构建密度感知卷积模块，从而有效地提取点间关键密度信息，加强初始输入特征的信息表达深度，提升算法捕获局部特征的能力。然后，构建空间特征自注意力模块，基于自注意力和空间注意力机制强化全局上下文信息和空间位置信息的关联性，对全局特征和局部特征进行有效聚合，从而提取更深层次的上下文特征，有效提升算法的分割性能。最后，在公开的S3DIS数据集和ScanNet数据集上进行了大量实验。实验结果表明，算法的平均交并比分别达到了69.11%和72.52%，与其他算法相比有明显提升，验证了所提算法有着良好的分割性能和泛化性能。

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.