A new urban sound classification method based on improved dual-channel one-dimensional convolutional neural network is proposed to improve the accuracy of urban sound classification and reduce the difficulty of model application. Firstly, the Fbank features of audio are flattened according to two different directions of the time frame and Mel frequency band to obtain one-dimensional data. Secondly, the two-dimensional convolution in the AlexNet model is replaced by one-dimensional convolution, and the model structure is improved. Moreover，according to different flattening methods, the receptive field of the first convolution is increased and the convolution step size is also increased to reduce the amount of feature data. Finally, a two-channel convolutional neural network model is designed using the modified AlexNet model and the decision fusion method. To verify the effectiveness of the proposed method, an urban sound classification experiment was carried out on the UrbanSound8K data set. The results show that the classification accuracy of the proposed method is 96.76%, and the size of the model can be effectively reduced, which is convenient for application in the scene with few storage and computing resources.