Point cloud fine-grained semantic segmentation， that is， object component segmentation， has important applications in industrial production， such as manipulator control， intelligent assembly， and object detection. However， due to the scattered form of point cloud data， the geometric features at the boundary of object parts are not obvious and the calculation process is difficult， resulting in the low precision of fine-grained segmentation， which makes it difficult to meet the production needs. For point cloud segmentation at the component level， this paper proposes a fine-grained semantic segmentation network to enhance the local saliency of point clouds. In the network， the context information of local data is constructed to improve the precision of fine-grained segmentation. The network establishes an improved farthest-point sampling algorithm using geometric curvature to enhance the feature computing ability of a local data subset of the point cloud and to create a multiscale high-dimensional feature extractor for extracting the high-dimensional features of different scales. In the process of computing the point cloud features， seq2seq was used， the attention mechanism was introduced， and the high-dimensional features of different scales were fused to obtain the context information of fine-grained semantic segmentation. Finally， the fine-grained segmentation accuracy was improved， particularly for the segmentation effect at the boundary.The experimental results show that the overall intersection and merging ratio of this network on the ShapeNet part dataset achieves 85.2%， while the accuracy rate achieves 95.6%. The network also has a certain generalization ability. This method is of great significance in the fine-grained semantic segmentation of three-dimensional objects.