Aiming at the shortcomings of traditional fault identification methods in fault information acquisition, this paper proposes a hybrid HVDC transmission system fault identification method based on wavelet packet energy spectrum and convolutional neural network (CNN), which effectively solves the problem of complex fault feature extraction of hybrid HVDC transmission system and improves the accuracy of fault line and the fault type identification. Firstly, the frequency domain information of the fault transient signal is extracted based on wavelet packet decomposition, and the energy characteristics of fault signal are obtained by energy spectrum. Secondly, according to the extracted energy feature information, the order of fault line and fault type is identified by CNN. Finally, through example verification and algorithm comparison, it is concluded that the proposed model has high fault identification accuracy, and has strong anti-noise interference and tolerance to transition resistance.