The existing networks are difficult to learn local geometric shape information of point clouds effectively， and have problems such as being unable to focus on important feature structure effectively and insufficient fusion. Therefore， a point cloud classification and segmentation network based on Dual Attention Mechanism （DAM） and multi-scale fusion was proposed. Firstly， in the data feature extraction stage， geometric positions and weights of the convolution kernels were adjusted using Geometric Adaptive Convolution （GAC） dynamically， so that it was able to adapt to local geometric structure of the point cloud data dynamically， thereby capturing local features more effectively. Secondly， in order to further improve the feature expression ability， the DAM was introduced to learn and adjust weights of the feature channels and spatial information automatically， thereby enhancing feature representation of the key points. Finally， feature information of different scales was connected for effective fusion to enhance the feature learning effect， thereby making the final feature representation richer and improving classification and segmentation accuracy of the network. Experimental results on ModelNet40， ShapeNet and S3DIS datasets show that the proposed network increases the Overall Accuracy （OA） and mean Intersection over Union （mIoU） compared with PointNet++ and DGCNN （Dynamic Graph Convolutional Neural Network）， improving the performance of point cloud classification and segmentation effectively.