In order to achieve targeted prevention of bird-related outages in power grid, this paper proposed a bird sound recognition method based on Mel spectrogram and convolutional neural network (CNN). The study established a sound signal sample set of 40 kinds of representative bird species related to the common faults and preprocessed the bird sound signals by framing, windowing, and denoising, thus to calculate the signal energy of each frame in each Mel filter. The Mel spectrograms of bird sound signals were extracted according to the mapping relationship between the energy and the color depth. Moreover, the Mel spectrograms of the bird species related to po-wer grid faults were used as inputs, and the CNN was used for feature extraction of the Mel spectrograms by perfor-ming the convolution-pooling process repeatedly. Several iterations of training were carried out to adjust the internal parameters of the network, thus to obtain the optimal model for bird recognition. A case study results show that the recognition accuracy of 40 bird species reaches 96.1%, and that the proposed method is better than other transfer learning models. This study can provide references for transmission line operators to identify the related birds correctly and carry out differential prevention of bird-related outages.