To address the inability of the existing machine learning methods to simultaneously consider both the temporal and spatial domain features of electroencephalogram(EEG)signals in classifying EEG features,a feature fusion based Attention-EEGNet-BiGRU(AEBGNet)is presented for classifying motor imagery(MI)EEG signals.AEBGNet is capable of fusing the temporal domain features extracted by convolutional neural network with attention mechanism and spatial domain features extracted by a bidirectional gated recurrent unit to obtain more distinctive spatiotemporal features.The constructed AEBGNet classification model achieves an average accuracy of 80.37%on the BCI competition IV 2b dataset,and there is an improvement of 6.09%over the standard EEGNet method.The results demonstrate the effectiveness of the proposed method in enhancing the classification accuracy of MI EEG signals,providing a new idea for MI EEG signal classification.