High-voltage circuit breaker operating mechanism vibration signal contains important information about the status of the circuit breaker, which is of great significance for the diagnosis and identification of the operating status of the operating mechanism. Aiming at the complex characteristics of random and non-smooth vibration signals, a circuit breaker fault diagnosis method based on bispectrum analysis and a two-stream flow shallow convolutional neural network is proposed. Bispectral analysis and wavelet analysis are performed on the vibration signal. The 2D bispectral matrix and 1D wavelet band energy are extracted as the dual-channel features of the two-stream convolutional neural network, respectively; supervised model training of vibration signals collected from circuit breaker simulation experiments for five operating conditions. The results show that the bispectral analysis can suppress Gaussian noise, retain the main peak morphological features of the operating mechanism under different operating conditions and fuse wavelet band energy features, and the proposed model can achieve a high recognition accuracy of 98.33% in 5 training iterations to achieve fault diagnosis and identification of the circuit breaker operating mechanism.