For the single kernel based clustering methods, the selection of kernel parameters largely affects the clustering results. To address this issue, a new multiple kernel fuzzy cmeans clustering algorithm is proposed, in which the maximum entropy method is used to regularize the kernel weights and decide the important kernels. A new objective function is developed to simultaneously minimize the within cluster dispersion in the kernel space and maximize the kernel-weightentropy. Thus, the optimal clustering results have been yielded and the important kernels are extracted according to the optimal assignment of kernel weights. Experiments on synthetic 'nonspherical' shaped datasets have demonstrated the efficiency and superiority of the presented algorithms.