An emotion state recognition method based on squeeze-and-excitation networks is proposed. Firstly, time-domain features are extracted from electroencephalogram(EEG) signals of different channels, and a three-dimensional feature matrix is constructed according to the relative position of electrode channels. Then, the squeeze-and-excitation networks are constructed by combining the squeeze-and-excitation block with the 3D convolutional neural network for high-level abstract feature extraction. Finally, fully connected layers are used for emotional state classification. The experiment is carried out on the DEAP data set. The experimental results show that squeeze-and-excitation networks can adaptively correct the attention of features and optimize the weight of each feature based on the time-domain saliency information and electrode spatial position information in the EEG signal. Meanwhile, the squeeze-and-excitation networks can also strengthen important features and improve the recognition accuracy by using the complementary information of different features. In addition, the squeeze operation of the squeeze-and-excitation networks can obtain the global information of the input data and have faster convergence speed.