The identification of the disturbance waveform is the precondition of the research work of feature extraction and information mining based on the disturbance. However, noise, interference between disturbances and the effect of feature extraction method may lead to edge blurring of features of the same domain extracted from different disturbances, thus affecting the accuracy of disturbance recognition. Therefore, this paper proposes an identification method based on multi-domain typical features to identify disturbance types. First, the neural network is trained successively with multi-domain and single-domain features, and then the output of network in each domain is fused based on DS evidence theory to set up a multi-domain features oriented identification algorithm. Next, on the basis of analyzing the influence of three factors on the single-domain features, the effectiveness of the proposed algorithm is demonstrated by comparing its identification accuracy with that of the traditional single-domain feature recognition algorithms. The method reduces the influence of anomalous features in a single domain on the identification accuracy, and is robust to noise and stable. This work is supported by the Science and Technology Project of the Headquarters of State Grid Corporation of China “Key Technologies Research and Application of Primary and Secondary Deep Integration with Lean Operation and Maintenance of Distribution Switch Based on Internet of Things” (No. 52130421000S).