Aiming at the problem of slow convergence and weak real-time processing of large data in general Fuzzy C-Means (FCM) algorithm, an improved method of penalty factor on sample membership was proposed. Firstly, the characteristics of Suppressed Fuzzy C-Means (SFCM) clustering were analyzed, and the trigger condition for adjusting sample membership by penalty factor was studied, and then the dynamic membership adjusting scheme of SFCM based on penalty factor was designed. By using the algorithm, the samples are "moved to the poles" to achieve the purpose of rapid convergence. Theoretical analysis and experimental result show that under the same initial condition, the execution time efficiency of the improved algorithm is increased by 40% and 10% respectively compared with the traditional FCM and Optimal-Selection-based SFCM (OS-SFCM), at the same time, the clustering accuracy is also improved.