Kernel possibilistic C-means (KPCM) clustering algorithm introduces kernel method into possibilistic clustering, which can effectively cluster hypersphere, noisy, and singular point data, but it has the center coincidence problem of possibilistic clustering. Therefore, the β cut-set is introduced into the KPCM clustering algorithm, and the typical values of some sample data are modified by generating clustering kernel to improve the relationship between classes. At the same time, a Kernel possibilistic C-means clustering algorithm based on cut-set threshold (C-KPCM) is proposed to overcome the defect of consistency clustering of KPCM clustering algorithm. Combined with the non-local spatial information of the image, the adaptive median filtering algorithm is used to adaptively adjust the filtering radius to generate new fuzzy factors, which are added to the objective function of C-KPCM clustering algorithm. The kernel possibilistic C-means clustering algorithm based on non-local spatial information enhances the robustness of the clustering algorithm under strong noise interference. Simulation results verify the effectiveness of the proposed algorithm.