The density-based spatial clustering of applications with noise (DBSCAN) algorithm is one of the clustering analysis methods in the field of data mining. It has a strong capability of discovering complex relationships between objects and is insensitive to noise data. However, existing DBSCAN methods only support the clustering of unimodal objects, struggling with applications involving multi-model data. With the rapid development of information technology, data has become increasingly diverse in real-life applications and contains a huge variety of models, such as text, images, geographical coordinates, and data features. Thus, existing clustering methods fail to effectively model complex multi-model data and cannot support efficient multi-model data clustering. To address these issues, in this study, a density-based clustering algorithm in multi-metric spaces is proposed. Firstly, to characterize the complex relationships within multi-model data, this study uses a multi-metric space to quantify the similarity between objects and employs aggregated multi-metric graph (AMG) to model multi-model data. Next, this study employs differential distances to balance the graph structure and leverages a best-first search strategy combined with pruning techniques to achieve efficient multi-model data clustering. The experimental evaluation on real and synthetic datasets, using various experimental settings, demonstrates that the proposed method achieves at least one order of magnitude improvement in efficiency with high clustering accuracy, and exhibits good scalability.