The classification and segmentation of point clouds are widely applicable in robotic navigation， virtual reality， and autonomous driving. Most current deep learning approaches for point cloud processing employ multilayer perceptrons （MLPs） with shared weights and single pooling operations to aggregate local features. This methodology often hinders the accurate representation of structural information within point clouds exhibiting complex arrangements. To address these challenges， a novel point cloud shape-adaptive local feature encoding method was proposed， aimed at effectively capturing the structural information of point clouds with diverse geometric configurations while enhancing classification and segmentation performance. Initially， an adaptive feature enhancement module was introduced， this module utilized differentiation and learnable adjustment factors to strengthen the feature representation， compensating for the descriptive limitations inherent in shared weight MLPs. Building on this foundation， a feature aggregation module was designed to assign variable weights to distinct points based on their absolute spatial distances. This approach facilitates adaptation to the variable shapes of point cloud structures， accentuates representative point sets， and enables a more precise depiction of local structural information. Experimental evaluations conducted on three extensive public point cloud datasets reveal that the proposed method achieves exceptional performance in both classification and segmentation tasks， attaining an overall instance average classification accuracy of 93.9% on the ModelNet40 dataset， along with mean intersection over union （mIoU） scores of 85.9% and 59.7% on the ShapeNet and S3DIS datasets， respectively.