To address issues such as voids and incomplete shapes in three-dimensional (3D) point clouds obtained during the current 3D reconstruction process, a multiscale hybrid feature extraction and activation query point-cloud completion network is proposed. This network adopts an encoder-decoder structure. To extract local information while considering the overall structure, a multiscale hybrid feature extraction module is proposed. The input point cloud was classified into different scales through downsampling, and the hybrid feature information of the point cloud was extracted at each scale. To maintain the high correlation of the point-cloud completion results, an activation query module that retains the feature sequences with high scores and strong correlations is proposed for scoring operations. After the feature sequences are passed through the decoder for point-cloud completion, a complete point cloud is obtained. Experiments on the public dataset PCN indicate that in the comparison of quantitative and visual results, the proposed network model achieves superior completion effects in point-cloud completion and can further enhance the quality of point-cloud completion.