To solve the problem that the traditional 3D laser point cloud scene segmentation algorithm tends to ignore the blurring of target boundaries,a 3D laser point cloud scene segmentation network is designed using a boundary contrastive learning algorithm,so as to improve the model’s prediction performance at the boundaries through contrastive learning.Firstly,the PointNet++ is taken as the backbone network,multi-scale downsampling feature encoding and upsampling feature decoding are used to learn the semantic features of different target categories in the point cloud,and the prediction of target categories is conducted point by point,so as to achieve overall scene segmentation.Then,a contrastive learning algorithm is introduced to capture the boundaries of sub-scene point clouds through iterations and mine fuzzy boundary points.Finally,the contrastive learning loss function is used to enhance the differentiation of boundary points belonging to different categories at the network training stage,which significantly improves the accuracy of 3D laser point cloud scene segmentation.A large number of experiments are conducted on the publicly available 3D laser point cloud scene segmentation dataset,and the results show that the proposed algorithm has the bests egmentation performance in 15 out of 19 semantic categories,with overall performance indicators superior to the comparison algorithms.The ablation experiments and visualization results also verify that the proposed algorithm can effectively improve the category prediction performance of boundary points in 3D laser point cloud scene segmentation tasks,which fully demonstrates the superiority of the proposed algorithm.