Electroencephalography (EEG) has become the most widely used tool for doctors to diagnose nervous system diseases.It is of great significance to realize automatic recognition of epileptic EEG signals of the clinical diagnosis and treatment of epilepsy patients.In order to improve the recognition precision,this paper proposes a kind of automatic recognition model based on multi-scale convolution feature fusion of epileptic EEG signals.First of all,the multi-scale convolution features fusion method is used to extract more granularity data and solve the problem of information complementation at different levels in convolutional neural network (CNN).Then,the temporal features are extracted by long short-term memory network (LSTM),and the final recognition results are given by softmax classifier.The experiment is completed on the Epilepsy Research Center at the University of Bonn experiment data set.The proposed model is compared with CNN-LSTM model, the single LSTM model,et al.The experimental results show that the recognition precision of the proposed method is higher than other method,the average accuracy is 99.19%.The model could recognize epileptic EEG category,has excellent recognition performance and clinical application potential.