As a new technology, extreme learning machine (ELM) has good generalization performance in regression and classification. Weighted fuzzy local information C-means (WFLICM) uses point coordinate distance and the local pixel coefficient of variation to mark the impact factor of each point to the middle point, improving the robustness of fuzzy C-means cluster algorithm. Based on ELM and improving WFLICM, new kernel weighted fuzzy local information C-means based on ELM (ELM-NKWFLICM) is proposed. The method is based on ELM feature mapping technique, mapping the original data to the high-dimensional ELM hidden space through the ELM feature mapping technique, and then is clustered by the new kernel weighted fuzzy local information c-means (NKWFLICM) of the improved new kernel local spatial information. Experimental results show that the proposed algorithm has better robustness than the WFLICM algorithm and retains the original image details well. The algorithm is more efficient in dealing with complex nonlinear data, and overcomes the sensitivity of fuzzy clustering algorithm to fuzzy exponents.