Point cloud completion aims to reconstruct a high-quality complete point cloud from incomplete point cloud data. However， most existing point cloud completion networks have limitations in capturing local features and reconstructing details， resulting in poor performance of the generated point cloud in terms of local details and completion accuracy. To address these issues， a multi-stage point cloud completion Network based on Adaptive Neighborhood Feature Fusion （ANFF-Net） was proposed. Firstly， the neighborhood selection of key points was adjusted by the feature extractor adaptively to adapt to different shapes of point clouds， so as to capture spatial relationships between points with different semantics effectively， thereby reducing loss of the local details. Then， a local perception Transformer was used by the feature expander to further expand local feature information of the neighboring points， thereby improving the network’s ability to recover details. Finally， a cross-attention mechanism was applied by the point cloud generator to propagate local feature information of the incomplete point cloud selectively， and a folding module was used to refine the local regions gradually， thereby enhancing detail retention of the completed point cloud significantly and generating more consistent geometric details. Experimental results show that ANFF-Net improves the average completion accuracy by 9.68% compared to ProxyFormer on the ShapeNet55 dataset and achieves good completion performance on the PCN and KITTI datasets. Visualization results indicate that the point clouds generated by ANFF-Net have finer granularity and are closer to the ground truth in shape.