The traditional hyperspectral image (HSI) clustering algorithm suffers from issues, such as poor accuracy. In addition, accurately measuring the similarity relationship between pixels using long-time and commonly used distance-measurement criteria is difficult. To improve the clustering performance of hyperspectral images, this study proposes a hyperspectral image clustering algorithm based on spectral unmixing and dynamic-weighted diffusion mapping. The algorithm is based on the decomposition of mixed pixels and diffusion distance, calculated using the diffusion mapping theory. The proposed method uses the high-dimensional geometry and abundance structure observed in hyperspectral images to solve the clustering problem. Experimental results on two real hyperspectral datasets showed that the proposed algorithm has high classification accuracy and can be successfully applied to hyperspectral image clustering.