In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a method for the identification of bird species related to power grid faults based on combined features and a Convolu? tional Neural Network (CNN). Firstly, based on the information from historical bird-related faults in the power grid and the investigation results of bird species around transmission lines, 13 high-risk, 8 low-risk, and 2 harmless bird species were selected to build a sound sample set. Then, the Mel-frequency Cepstrum Coefficients (MFCC), Gamma? tone Frequency Cepstrum Coefficients（GFCC），and Short-term Energy (STE) features of bird sounds were extracted after preprocessing such as framing, windowing, noise reduction, and clipping. To solve the problem of insufficient ex? pression ability of a single feature set, a new sound feature set was generated combining MFCC, GFCC, their firstorder differences, and STE features after normalization. Finally, a CNN was built to train and recognize the combined features. The identification accuracy of the test set reaches 91.8%, which is better than those with a single MFCC and GFCC feature set