Abstract: Due to the influence of underground coal dust and easy obstruction, the laser point cloud data of hydraulic supports is prone to be incomplete. The existing point cloud segmentation algorithms are difficult to obtain fine-grained point cloud features, unable to obtain complete structural information of the point cloud. The algorithms are prone to introducing semantically dissimilar points in the neighborhood, resulting in low precision of laser point cloud segmentation for hydraulic supports. In order to solve the above problems, a laser point cloud segmentation algorithm for hydraulic supports based on neighborhood feature encoding and optimization is proposed. The method introduces a local neighborhood feature aggregation module consisting of neighborhood feature encoding module, neighborhood feature optimization module, and hybrid pooling module. The neighborhood feature encoding module adds polar coordinate encoding and centroid offset to represent the spatial structure of local point clouds on the basis of traditional 3D coordinate encoding, improving the feature extraction capability for incomplete point clouds. The neighborhood feature optimization module optimizes the feature expression in the neighborhood space by judging the feature distance and discarding redundant features, thereby more effectively learning the local fine-grained features of the point cloud and enhancing the local contextual information of the point cloud. The hybrid pooling module combines attention pooling and max pooling to obtain single point features with rich information by aggregating salient and important features within the neighborhood, reducing information loss. A neighborhood expansion module consisting of two sets of local neighborhood feature aggregation modules and residual connections is constructed to capture long-range dependencies between features, expand the local receptive field of individual points, and aggregate more effective features. The experimental results show that the algorithm has an mean intersection over union of 93.26% and an average accuracy of 96.42% on the laser point cloud segmentation dataset of hydraulic supports. It can effectively distinguish different geometric structures of hydraulic supports and achieve accurate segmentation of various components of hydraulic supports.