The invention discloses an urban noise identification method based on deep network migration features and augmented self-encoding. The method comprises the following steps: 1, preprocessing each typeof collected urban noise signals, including denoising, framing and windowing; 2, converting the processed noise signal into a spectrogram; 3, performing feature extraction on the spectrogram obtainedin the step 2 by using a plurality of pre-trained deep convolutional neural networks; 4, fusing the obtained feature vectors x by using an augmented auto-encoder; 5, on the basis of the fusion features in the step 4, constructing a multilayer one-class classification model; 6, calculating an output weight and a decision threshold value of ML-OCRLS; and 7, carrying out classification prediction onunknown signals. The hidden layer neurons of the augmented auto-encoder provided by the invention can adjust and optimize all features, main information can be extracted based on ML-OCRLS of the augmented auto-encoder, feature redundancy is reduced, and meanwhile, multiple transfer learning features are effectively fused, so that the classification precision of a classifier is improved.