A novel action recognition method based on general multiple kernel learning is proposed. Firstly, histogram of oriented gradients (HOG） based on edge of image and scale invariant feature transform（SIFT） based on dense sampling are extracted. Furthermore, spatial pyramid model is considered to obtain coarse spatial information. Then, the kernel matrix of each level in spatial model is computed by histogram intersection kernel function. With general multiple kernel learning, the weights of kernel matrixes are solved and the optimal kernel matrix is achieved by the linear combination of kernel matrixes. Finally, action recognition is realized by the decision function. The obtained impressive result shows that the proposed algorithm is more effective than some common methods in Willow-actions dataset.