In robotic environmental perception tasks， single-view point clouds suffer from severe geometric information loss due to sensor viewpoint limitations. Point cloud reconstruction methods based on Computer-Aided Design （CAD） model replacement can avoid the risks of structural instability associated with reconstruction directly from point clouds by retrieving similar models and applying deformation. Unsupervised 3D shape REtrieval and Deformation （U-RED） algorithm achieves topologically consistent CAD model replacement while maintaining the editability of the reconstruction results. However， when dealing with objects with complex topology， it still faces problems such as insufficient rotation and translation invariance in point cloud representations， difficulty in distinguishing neighboring components due to geometric similarity among homologous components， and parameter update failures caused by scattered attention weights and gradient vanishing or explosion. To address these challenges， a Vector neuron enhanced Unsupervised REtrieval and Deformation algorithm with Feature affine residual （VU-RED-F） was proposed based on U-RED. Firstly， a Vector Neuron Encoder （VNE） was constructed to improve the robustness of the feature extraction module in representing rotation and translation invariance of point clouds. Secondly， learnable affine transformation residuals were introduced to reconstruct the feature mapping process， adaptively adjust the feature distribution， and enhance the network's ability to discriminate local geometric structures between components. Finally， by integrating soft-threshold gating and residual correction， the stability of gradient propagation was enhanced while constraining the sparsity of the attention distribution， thereby boosting network convergence efficiency and reducing loss during retrieval and deformation. Experimental results on the synthetic PartNet and ComplementMe datasets， as well as the real Scan2CAD dataset， show that the VU-RED-F algorithm has the lowest average chamfer distance （cd） loss， improving the fidelity of local geometric details in CAD models.