Abstract: Current deep learning methods do not take the mechanism of brain function separation and integration into consideration for EEG recognition, resulting the poor recognition accuracy. In view of this, we propose an adaptive multi-view deep learning model which combines multi-view learning and adaptive weights learning mechanism. By dividing the EEG signals into multiple local perspectives according to different brain regions, and regarding the entire brain area as global perspective, a multi-view deep learning framework that can reflect the mechanism of brain function separation and integration was constructed. The attention mechanism was used to adaptively learn the weights of multiple views. The proposed learning model not only could learn deep features of EEG signals in different brain regions, but also could adaptively learn the weights of multiple views. Experimental results on public EEG dataset and self-collected EEG dataset demonstrate the effectiveness of the proposed method.