Effective acquisition of point cloud features is the key to analyzing and processing 3D point cloud scenes. To address the problem that current deep learning methods have inadequate feature information extraction and difficulty in capturing deep semantic information， a fusion fine-grained feature encoding network is proposed to improve the accuracy of point cloud classification and segmentation tasks. First， the feature extraction module contains two sub-modules， one is the dilation graph convolution module， which can extract richer geometric information than graph convolution； and the other is the fine-grained feature encoding module， which can capture detailed features of local regions. Second， the two modules are dynamically fused by learnable parameters to efficiently learn the contextual information of each point. Finally， all the extracted features are summed and pass the channel-wise affinity attention module， assisting the feature map to avoid redundancy by emphasizing its distinct channels. Point cloud classification experiment is performed on the ModelNet40 and ScanObjectNN datasets， and the overall accuracy is 93.3% and 80.0%， respectively. The mean intersection over union （mIoU） is 85.6% for part segmentation experiments on the ShapeNet Part dataset. Experimental results show that the proposed method performs better than the current mainstream methods.