Abstract: Brain-computer Interface (BCI) technology based on motor imagery (MI) can establish communication between the human brain and outside world. It has been widely used in medical rehabilitation and other fields. Owing to the characteristics of the motor imagery EEG signals，such as non-linear, non-stationary, and low signal-noise ratio, it is a huge challenge to classify motor imagery EEG signals accurately. Hence, we propose a novel multiscale feature extraction and squeeze-excitation model which is applied for the classification of motor imagery EEG signals. Firstly, the proposed deep learning module, which is based on multiscale structure, automatically extracts time domain features, frequency domain features and time-frequency domain features. Then, the residual module and squeeze-excitation module are applied for feature fusion and selection, respectively. Finally, fully connected network layers are used to classify motor imagery EEG signals. The proposed model is evaluated on two public BCI competition datasets. The results show that the proposed model can effectively improve the recognition performance of motor imagery EEG signals compared with the existing several state-of-the-art models. The average accuracy on the two datasets is 78.0% and 82.5%, respectively. Moreover, the proposed model has higher application value because it classifies motor imagery EEG signals efficiently without manual feature extraction when spatial information is insufficient.