Abstract: For the sound environment dataset of the DCASE 2017 Challenge, Mel frequency cepstral coefficients (MFCC), short-time energy (SE), acoustic event likelihood features (AELF), and mute time (MT) features were extracted to form a multi-features fusion matrix. Comparing various kernel functions and optimization algorithms, radial basis function kernel (RK) was finally selected to establish the support vector machine (SVM) model, and cross validation (CV) method was utilized to optimize SVM parameters and to classify 15 acoustic scenes. The experimental results show that the classification accuracy of grocery store and office can reach more than 90%, and the average classification accuracy reaches 71.11%, which is much higher than the average classification accuracy of 61% of the baseline system given in the challenge.