Multiple Kernel Learning and Support Vector Machine (MKL-SVM) was applied to automatic music genre classification to choose the optimal kernel functions for different features, a method of conducting the optimal kernel function combination into the synthetic kernel function by weighting for music genre classification was proposed. Different optimal kernel functions were chosen for different acoustic features by multiple kernel classification learning, the weight of each kernel function in classification was obtained, and the weight of each acoustic feature in the classification of the genre was clarified, which provided a clear and definite result for the analysis and selection of the feature vector in the classification of music genre. The experiments on the dataset of ISMIR 2011 show that, compared with the traditional single kernel support vector machine classification, the accuracy of the proposed music genre automatic classification method based on MKL-SVM is greatly improved by 6.58%. And the proposed method can more clearly reveal the the different features' impacts on music genre classification results, the classification results has also been significantly improved by selecting features with larger effects on classification.