Aiming at the single classification mode of the existing epilepsy detection algorithms， an automatic detection method of epilepsy ElectroEncephaloGram （EEG） based on Residual Network （ResNet） was proposed. Firstly， a one-dimensional ResNet with three residual blocks was built to extract the intrinsic features of EEG signals. Secondly， the fully connected network was used for classification. Finally， the proposed method was tested on the epilepsy EEG database of University of Bonn with seven two-class， five three-class and five-class of all data EEG recognition problems studied， and the detection accuracies of the proposed method were 96.19%-99.32%， 95.28%-97.45%， and 82.34%， respectively. Experimental results show that the proposed method has better universality and classification accuracy， is more suitable for the practical application requirements.