The traditional environmental sound classification model does not fully extract the features of environmental sound, and the full connection layer of conventional neural network is easy to cause over-fitting when the network is used for environmental sound classification. In order to solve the problems, an environmental sound classification method combining with Mel-Frequency Cepstral Coefficient (MFCC), deep convolution and Bagging algorithm was proposed. Firstly, for the original audio file, the MFCC model was established by using pre-emphasis, windowing, discrete Fourier transform, Mel filter transformation, discrete cosine mapping. Secondly, the feature model was input into the convolutional depth network for the second feature extraction. Finally, based on reinforcement learning, the Bagging algorithm was adopted to integrate the linear discriminant analyzer, Support Vector Machine (SVM), softmax regression and eXtreme Gradient Boost (XGBoost) models to predict the network output results by voting prediction. The experimental results show that, the proposed method can effectively improve the feature extraction ability of environmental sound and the anti-over-fitting ability of deep network in environmental sound classification.