Aiming at the problem that traditional hyperspectral image clustering algorithms are difficult to effectively deal with hyperspectral images with rapidly increasing data volume, a hyperspectral clustering algorithm based on hyper-pixel anchor layer convergence point selection is proposed. SuperPCA is used to reduce the dimension of original data based on super pixel cutting. Selecting representative anchor points by K-means, and constructing an adjacency matrix based on anchor points. The similarity graph is constructed by the method of non-nuclear neighbor assignment to avoid the adjustment of thermonuclear parameters. Finally, spectral clustering analysis is carried out to obtain clustering results. The simulation experiments on Indian Pines and Pavia Centre hyperspectral data sets show that the classification map obtained by this algorithm contains fewer false points, and the distribution of ground objects is smoother. Compared with the current hyperspectral image clustering algorithms, this algorithm has a better clustering effect.