Aiming at the problem that it is difficult to distinguish similar land types in outdoor scenes with multiple objects and complex spatial topological relationships， an A-Edge-SPG （Attention-EdgeConv SuperPoint Graph） graph neural network combining graph model and attention mechanism module was proposed. Firstly， the superpoints were segmented by the combination of graph cut and geometric features. Secondly， the local adjacency graph was constructed inside the superpoint to capture the context information of the point cloud in the scene and use the attention mechanism module to highlight the key information. Finally， a SuperPoint Graph （SPG） model was constructed， and the features of hyperpoints and hyperedges were aggregated by Gated Recurrent Unit （GRU） to realize accurate segmentation among different land types of point cloud. On Semantic3D dataset， the semantic segmentation effect of A-Edge-SPG model and SPG-Net （SPG neural Network） model was compared and analyzed. Experimental results show that compared with the SPG model， A-Edge-SPG model improves the Overall segmentation Accuracy（OA）， mean Intersection over Union （mIoU） and mean Average Accuracy （mAA） by 1.8， 5.1 and 2.8 percentage points respectively， and significantly improves the segmentation accuracy of similar land types such as high vegetation and dwarf vegetation， improving the effect of distinguishing similar land types.