A simplified deep learning model was proposed to solve the problem of recognition based on the strong randomness and rapid dynamic change of EEG(epilepsy electroencephalogram) signals. The proposed model utilizes one-dimensional convolutional neural network, which simplifies the convolutional layers and pooling layers to improve the efficiency. Based on the overall Keras framework, the RMSProp algorithm was used for the model in the training process, and the algorithm estimated the loss through a predefined objective function. The model design incorporated a batch normalization layer and a global mean pooling layer. The EEG recognition was researched from two aspects based on the proposed model:with empirical mode decomposition, the first three orders, the first five orders, the first seven orders, and the first eight orders of intrinsic mode functions were selected for comparative analysis on the simplified model. Because of deep learning characteristics, the proposed model can directly recognize the original EEG signals without feature extraction. After extracting 7 types of features,it adds three different methods to compare the accuracy. The experimental results show that:the recognition rate of the first three orders of intrinsic mode function reaches the level of 92.1% for the five different types of EEG signals, which is higher than that of other features. The first eight orders′ recognition rate is lower than the original signal, which indicates that data preprocessing will lead to the noise. The proposed simplified deep learning model can effectively deal with the epileptic EEG recognition problem with higher efficiency and better performance.