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In plant phenotypic analysis, plant organ segmentation is the key to achieve automatic, accurate, non-destructive and high-throughput phenotypic parameter measurement. Traditional plant organ segmentation methods rely on experience to manually set parameters and adjust algorithms, but the existing deep learning based segmentation methods have insufficient ability to express local and global features. In order to solve these shortcomings, a semantic segmentation network for three-dimensional point clouds was proposed based on attention mechanism. In the coding stage, a local feature extraction method based on attention mechanism was proposed, which made full use of multilevel point cloud features and improved the ability of network to extract local feature of point cloud. In the decoding stage, a double nearest neighbor interpolation upsampling method combining feature distance and spatial distance was proposed to recover the lost point cloud features in downsampling more accurately, and further enhanced the expression ability of local features. At the same time, the channel and multi-head spatial self-attention mechanism were introduced to enhance the attention of the network to some important channels and ability to capture the global geometric structure, and improve the expression ability of the network for global features. Experimental results on a variety of plant point cloud datasets showed that the mean intersection over union of semantic segmentation of the proposed method reached 87.32%, 79.68%, 94.73%, 91.43%, 95.02%, respectively, which were better than those of popular deep learning networks such as DGCNN, PointCNN, ShellNet and so on. Cross validation experiments and ablation experiments were carried out to confirm the generalization and effectiveness of the network. Relevant experiments were carried out on ShapeNet dataset, and the network also achieved good segmentation results on other non plant 3D point cloud target semantic segmentation tasks.