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Early failures of aero-engine bearings are mostly caused by cracks, fatigue spalling and cage damage. These types of failures will produce instantaneous shocks in engine vibration signals. However, in the early faults, the vibration signal is excited by the coupling of too many components, and the defect impact signal is difficult to identify, and the early fault diagnosis is very difficult. This paper proposes a feature extraction method for early impact faults of aero-engine bearings based on convolutional self-encoding networks. By analyzing the periodicity of the impact components in the signal, using the translation invariant learning characteristics of the convolutional autoencoding network, the periodic components in the signal are automatically captured, and the signal is decomposed into multiple characteristic components reconstructed by the convolution kernel to realize the signal characteristics The self-learning of the components takes into account the characteristics of the kurtosis index describing the impact components of the signal, and the kurtosis index is used as the selection index of the optimal feature components to realize the extraction of early impact fault features. Finally, simulation data and bearing data are used to verify the effectiveness of the method.