Abstract: Sketch-based 3D model retrieval is an important way to retrieve 3D models in mechanical part library. There are huge modal differences between sketches and 3D models. Aiming at the information redundancy of multiple views projected by the 3D model, the weak representation ability of the features extracted from the part views, in order to improve the effect of sketch-based 3D model retrieval, a sketch-based 3D mechanical part Library retrieval method is proposed to fuse the local and global dual-scale features of the part model. Firstly, a projection view representation method of 3D model based on image entropy is proposed to reduce the redundancy among multiple views. Then, a mechanical sketch model for edge contour extraction of part view is proposed, and the contour sketch of the part is extracted in depth to approximate the similarity between the view and the sketch. Finally, a visual word bag model and an improved multi-view convolutional neural network model is constructed to extract the local and global features of the contour sketch respectively. By fusing the dual -scale features, Euclidean distance is used to match the 3D model of the part based on the sketch. The experimental results on ESB datasets show that the proposed method is significantly improved in a series of evaluation indicators such as accuracy and recall rate compared with the existing 6 common methods, which verifies the feasibility and effectiveness of the proposed method.