Sensor noise interference， uneven point cloud density， complex and diverse scenes， and the occlusion between objects pose great challenges to the research of the semantic segmentation of 3D point cloud scenes. In the view of the uneven sampling density of 3D point cloud data and limited depth of graph convolutional networks， this paper proposes a density adaptive method， which uses a multi-layer perceptron to learn a weight function， and uses kernel density estimation to learn a density function. The convolution operation is performed on the evenly sampled point cloud data. At the same time， inspired by deep learning in the image field， residual connection， hole convolution， and other structures were introduced to train deeper point cloud segmentation networks. The algorithm achieves an excellent performance on standard data sets of multiple point cloud segmentation.