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Due to the popularity of computer games and animation, research on 3D articulated geometry model retrieval has attracted a lot of attention in recent years. However, most existing works extract high-dimensional features to represent models and suffer from practical limitations. First, misalignment in high-dimensional features may produce unreliable euclidean distances and affect retrieval accuracy. Second, the curse of dimensionality also degrades efficiency. In this paper, we propose an embedding retrieval framework to improve the practicability of these methods. It is based on a manifold learning technique, the Diffusion Map (DM). We project all pairwise distances onto a low-dimensional space. This improves retrieval accuracy because intercluster distances are exaggerated. Then we adapt the Density-Weighted Nyström extension and further propose a novel step to locally align the Nyström embedding to the eigensolver embedding so as to reduce extension error and preserve retrieval accuracy. Finally, we propose a heuristic to handle disconnected manifolds by augmenting the kernel matrix with multiple similarity measures and shortcut edges, and further discuss the choice of DM parameters. We have incorporated two existing matching algorithms for testing. Our experimental results show improvement in precision at high recalls and in speed. Our work provides a robust retrieval framework for the matching of multimedia data that lie on manifolds.