Lidar-scanned point cloud data often suffer from missing information, and most existing point cloud completion methods struggle to reconstruct local details because of the sparse and unordered nature of the data. To address this issue, this paper proposes an attention-enhanced multiscale dual-feature point cloud completion method. The multiscale dual-feature fusion module is designed by combining global and local features, to improve completion accuracy. To enhance feature extraction, an attention mechanism is introduced to boost the network's ability to capture and represent key feature points. During the point cloud generation phase, a pyramid-like decoder structure is used to progressively generate high-resolution point clouds, preserving geometric details and reducing distortion. Finally, a generative adversarial network framework, combined with an offset-position attention discriminator, further enhances the point cloud completion quality. The experimental results show that the complementary accuracy of this method on the PCN dataset improves by 11.61% compared to that of PF-Net, and the visualization results are better than those of other methods in comparisons, which verify the effectiveness of the proposed network.