In multi-view clustering based on view weighting, weight value of each view products great influence on clustering accuracy. Aiming at this problem, a multi-view clustering algorithm named Entropy Weighting Multi-view Kernel K-means (EWKKM) was proposed, which assigned a reasonable weight to each view so as to reduce the influence of noisy or irrelevant views, and then to improve clustering accuracy. In EWKKM, different views were firstly represented by kernel matrix and each view was assigned with one weight. Then, the weight of each view was calculated from the corresponding information entropy. Finally, the weight of each view was optimized according to the defined optimized objective function, then multi-view clustering was conducted by using the kernel K-means method.The experiments were done on the UCI datasets and a real datasets. The experimental results show that the proposed EWKKM is able to assign the optimal weight to each view, and achieve higher clustering accuracy and more stable clustering results than the existing cluster algorithms.