Aiming at the inaccuracy of clustering numbers and the slow speed of ordinary consensus clustering algorithms, Newman greedy algorithms of complex networks theory and spectral clustering algorithms were combined to propose a novel consensus clustering algorithm based on Minkowski distance. The algorithm depicts the similarity between samples in terms of Minkowski distance and adopts the strategy of random walk. By adjusting the parameters of the Laplacian distance, the accurate information of the clustering number is automatically obtained. The simulation results show that the proposed consensus clustering algorithm based on Minkowski distance has the superiority of the running time and accuracy of the clustering number. This method was applied to actual copper froth flotation process, and the results further illustrated its effectiveness.