Large-scale hyperspectral image clustering algorithms are widely used in the field of remote sensing, including K-means clustering and spectral clustering algorithms. However, the spectral clustering algorithm still has its limitations. Because of its high computational complexity, it is not suitable for large-scale problems. The spectral clustering algorithm based on the anchor graph can reduce the computational cost to a certain extent. However, in the large-scale hyperspectral image data processsing, the anchor points need to be dense enough, otherwise reasonable accuracy cannot be obtained. This makes the computing cost of the clustering algorithm increase sharply. In order to overcome these problems, a new fast spectral clustering algorithm based on multi-layer bipartite graph is proposed. Firstly, the anchor points are selected by the binary tree,and the multi-layer anchor points are selected to construct the multi-layer anchor point graph. Then a multi-layer bipartite graph is constructed, and finally the spectrum of the graph is analyzed. The high efficiency of the proposed algorithm is proved by experiments.