Soft composites exhibit significant potential in advanced engineering applications but face critical computational challenges due to their inherent heterogeneity and geometric nonlinearity. Traditional meso-scale finite element analysis suffers from low efficiency, rendering macro-meso coupled multiscale analysis impractical for real-world engineering scenarios. To address this limitation, this study develops a clustering-based reduced-order homogenization method that synergistically integrates reduced-order homogenization techniques with clustering analysis, achieving remarkable computational efficiency while maintaining sufficient accuracy. First, we establish a two-scale analysis framework for soft composites on the basis of finite deformation theory. On the meso-scale, an energy density function is used to describe the constitutive behavior of the micro constituents. Then, we perform clustering analysis on the microscale representative volume element (RVE) to partition it into uniform subdomains called clusters. The clustering analysis groups regions with similar mechanical behavior and thereby reduces the system's complexity and related computational cost. After that, proper orthogonal decomposition (POD) is employed to generate reduced bases for approximating the mesoscopic deformation gradient fields. An efficient sampling strategy is used for both snapshot generation and model validation. A clustered version of reduced-order model (CROM) is established based on the principle of minimum energy. Numerical examples demonstrate that the developed CROM can maintain a high level of accuracy while achieving a computational acceleration of about 10⁴ compared to traditional finite element methods. A comparison to an existing clustering approach named self-consistent clustering analysis (SCA) is also given. Although the computational cost of the offline phase for the CROM is relatively high, the online analysis is rather fast. This significant improvement in efficiency makes the method highly suitable for problems that require frequent microscale RVE predictions, such as multiscale analysis or multiscale parameter identification. In conclusion, the developed CROM offers a promising and practical tool for engineers, which can be further applied in the design, optimization, and analysis of soft composites.