As a frequently personalized recommendation algorithm of the currently recommendation system, collaborative filtering uses the item evaluation by the approximate users to recommend. Kernel function is an approach for non-linear pattern analysis problems. Ordinarily, collaborative filtering will choose some different kernel functions to analyse the influence between the users. Since the single kernel function can not be adapted to the complicated and various scene, the combination of multiply kernel function becomes a solution. In terms of scenes, multiply kernel learning can combine every kernel function for a better result. This paper proposes a collaborative filtering algorithm based on multiple kernel learning. Based on the available kernel function, this algorithm optimizes the weights of every kernel function to match the data distribution. The experimental result on dianping dataset and foursquare dataset shows that compared with the collaborative filtering algorithm based on common similarity, the collaborative filtering algorithm based on multiple kernel learning achieves better performance. That is, multiple kernel learning has a better common adaptation.