Abstract: Aiming at the problem that most deep learning models lack the ability to obtain local features, a point cloud classification and segmentation network (FCA-Pointnet ++) based on local feature aggregation and driven point cloud sampling was proposed. A local feature aggregation module was designed and a Feature channel attention (FCA) module was constructed to enhance relevant local features and suppress irrelevant features. A driver point cloud sampling network (DPN) which could change the distribution of initial sampling points according to different tasks was designed. A channel-wise gate module, which could extract local features more accurately and refine features adaptively, was constructed. In the classification task, FCA-Pointnet ++ achieved an overall accuracy (OA) of 95.3% and 93.4% on ModelNet10 and ModelNet40, respectively. In the segmentation task, the mean intersection over Union(mIoU) of FCA-Pointnet ++ reached 86.1%, and the effectiveness of FCA-Pointnet ++ in classification and segmentation tasks was verified by experiments.