To implement the point cloud shape completion in a coarse-to-fine manner, an end-to-end two-stage multi-scale feature fusion network is proposed, in which each stage consists of an encoder-decoder. In the first stage, the set abstraction module extracts the global features of the incomplete point cloud, which can focus on more local neighborhood features while acquiring point features of different resolutions. A decoder built from multilayer perceptrons generates a coarse skeleton. In the second stage, the coarse skeleton and incomplete point cloud are used to learn multi-scale local features. The multi-scale local features are fused with the multi-scale global features of the first stage via an attention mechanism so that each point contains global and local geometric information. Finally, the global features and multi-scale local features are progressively upsampled, and a fine-grained complete point cloud is generated via multilayer perceptrons. The point cloud completion experiments on ShapeNet, MVP, and Completion3D datasets show that the chamfer distance is reduced by 17.1%, 3.9%, and 13.9% compared with the baseline, respectively, demonstrating the effectiveness of the proposed method.