The existing soft subspace clustering algorithm is susceptible to random noise when MR images are segmented, and it is easy to fall into local optimum due to the choice of the initial clustering centers, which leads to unsatisfactory segmentation results. To solve these problems, this paper proposes a soft subspace algorithm for MR image clustering based on fireworks algorithm. Firstly, a new objective function with boundary constraints and noise clustering is designed to overcome the shortcomings of the existing algorithms that are sensitive to noise data. Next, a new method of calculating affiliation degree is proposed to find the subspace where the cluster is located quickly and accurately. Then, adaptive fireworks algorithm is introduced in the clustering process to effectively balance the local and global search, overcoming the disadvantage of falling into local optimum in the existing algorithms. Comparing with EWKM, FWKM,FSC and LAC algorithms, experiments are conducted on UCI datasets, synthetic images, Berkeley image datasets, as well as clinical breast MR images and brain MR images. The results demonstrate that the proposed algorithm not only can get better results on UCI datasets, but also has better anti-noise performance. Especially for MR images, high clustering precision and robustness can be obtained, and effective MR images segmentation can be achieved.