HyperSpectral Image （HSI） consists of hundreds of bands with strong intra-band correlations between bands and high redundancy， resulting in dimensional disaster and increased classification complexity. Therefore， a Deep Adversarial Subspace Clustering （DASC） method was used for hyperspectral band selection， and Laplacian regularization was introduced to make the network performance more robust， which reduces the classification complexity under the premise of ensuring classification accuracy. A self-expressive layer was introduced between the encoder and the decoder to imitate the “self-expression” attribute of traditional subspace clustering， making full use of the spectral information and nonlinear feature transformation to obtain the relationships between the bands， and solving the problem that traditional band selection methods cannot consider spectral-spatial information simultaneously. At the same time， adversarial learning was introduced to supervise the sample representation of the auto-encoder and subspace clustering， so that the subspace clustering has better self-expression performance. In order to make the network performance more robust， Laplacian regularization was added to consider the manifold structure reflecting geometric information. Experimental results on two public hyperspectral datasets show that compared with several mainstream band selection methods， DASC method has higher accuracy， and the selected band subset of the method can satisfy application requirements.