In recent years, research on emotion recognition has no longer only focused on facial and voice recognition, and emotion recognition according to electroencephalogram (EEG)-based physiological signals is becoming increasingly popular. However, due to the incomplete extraction of feature information or the maladjustment of classification models, the classification effect of emotion recognition is poor. Therefore, this study proposes a hybrid model combining differential entropy (DE), convolutional neural network (CNN), and gated recurrent unit (GRU), namely, DE-CNN-GRU, to study EEG-based emotion recognition. The pre-processed EEG signals are divided into five frequency bands, and their DE features are extracted as preliminary features, which are then input to the CNN-GRU model for deep feature extraction and further classified by using Softmax. The hybrid model is tested on the SEED dataset. The result shows that the average accuracy obtained by the hybrid model is 5.57% and 13.82% higher than that obtained by using the CNN or GRU algorithm, respectively.