Hyperspectral image (HSI) clustering has attracted a great deal of attention, owing to lower cost and higher application prospects. Deep subspace clustering has been proved to be an effective method to explore the sample relationship of HSI clustering. However, due to the complex distribution of HSI data, the problem of data cluster overlap occurs frequently. In the actual sample distribution, a sample may belong to multiple subspaces. The complex sample distribution brings challenges to subspace clustering. In this letter, we propose a deep mutual information subspace clustering (DMISC) network to find a more intuitive feature space for nonlinear subspace clustering. Technically, we maximize the mutual information between the samples and their generated features to enlarge the interclass dispersion and intraclass compactness. The deep subspace method can find a more suitable nonlinear intrinsic relationship, benefitting from the generated feature distribution. We evaluate DMISC on four HSI datasets and compare the performances with 12 popular clustering methods. The experimental results demonstrate that our method outperforms many prior unsupervised methods.