Emotion, as a high-level function of the human brain, has a great impact on people’s mental health. To fully consider EEG signals’ spatial information and time-frequency information, and realize humancomputer interaction better. This paper proposes an improved DenseNet emotion recognition model based on 3D feature map. By extracting the differential entropy features of the θ, α, β and γ frequency bands of the EEG signals, and combining the position mapping relationship of the EEG channel electrodes, a threedimensional feature map is constructed, and then the improved densely connected convolutional network (DenseNet) is used for secondary feature extraction and classification. To verify the effectiveness of this method, a classification experiment including positive, neutral and negative emotions is carried out on the SEED data set. The classification accuracy rates obtained in the single-subject experiment and the all-subject experiment are 98.51% and 98.68%, respectively. The experimental results show that the method of 3D feature map combined with feature reuse can get high-precision classification results, which provides a new direction for emotion recognition.