Semantic segmentation for outdoor large scale point cloud has become a key technology for 3D scene understanding and environmental awareness and is widely used in fields such as autonomic driving,intelligent robotic and augmented reality (AR).However,laser point clouds of large scenes are characterized by multi targets,complex geometrical structures,and large variations in the scales of different features,making the segmentation performance on sparse point clouds of small targets (e.g.,pedestrians,motorcycles,etc.)low.To address the above problems,an outdoor point cloud semantic segmentation algorithm incorporating an attentive gating mechanism is proposed in this paper.An attentive Gating Unit based on attention mechanism and multi scale feature fusion method is designed to improve the expression of fine grained features of laser point clouds and significantly reduce the information loss during the random downsampling process,thus enhancing the feature extraction performance for weak targets.At the same time,anaverage pooling unit based on shared MLP is designed to further simplify the self attention local feature aggregation module,which effectively accelerates the network convergence speed and can efficiently realize the semantic segmentation of point clouds in large scenes.The experiments on outdoor driving dataset semanticKITTI show that the convergence speed is increased by 48.3%,and the mean intersection over Union (mIoU)of all classes is improvedfrom 53.9% to 54.5%,an increase of 0.6%,compared with the literature RandLA Net.Especially,the Intersection over Union (IoU)of small scale class is significantlyimproved,for example,the IoU score of person and motorcycle are increased by 0.8% and 5.4%,respectively.