Engineering projects rely on complex 3D CAD models throughout their life cycle. These 3D models comprise millions of geometries that impose storage, transmission, and rendering challenges. Previous works have successfully employed shape-matching techniques based on deep learning to reduce the memory required by these 3D models. This work proposes a graph-based algorithm that improves unsupervised clustering in deep feature space. This approach dramatically refines shape-matching accuracy and results in even lower memory requirements for the 3D models. In a labeled dataset, our method achieves a 95 percent model reduction, outperforming previous unsupervised techniques that achieved 87 percent and almost reaching the 97 percent reduction from a fully supervised approach. In an unlabeled dataset, our method achieves an average model reduction of 87 percent versus an average reduction of 77 percent from previous unsupervised techniques.