Motor imagery (MI) EEG signals are more difficult to recognize due to the inclusion of long, continuous eigenvalues as well as their own strong individual variability and low signal-to-noise ratio. In this study, we propose a model that combines a con-volutional neural network (CNN) with a Transformer, aiming to effectively decode and classify motor imagery EEG signals. The method takes the original multichannel motor imagery EEG signals as input, and learns the local features of the entire one-dimensional temporal and spatial convolutional layers by firstly performing a convolutional operation on the temporal domain of the signals in the first temporal convolutional layer, and then subsequently performing a convolutional operation on the null domain of the signals in the second spatial convolutional layer. Next, the temporal features are smoothed by averaging the pooling layers along the temporal dimension and passing all the feature channels at each time point to the attention mechanism to extract the global correlations in the local temporal features. Finally, a simple classifier module based on a fully connected layer is used to classify the EEG signals for prediction. Through experimental validation on the publicly available BCI competition dataset IV-2a and dataset IV-2b, the results show that the model can effectively classify MI EEG signals with average classification accuracies of up to 80.95% and 84.79%, which is an improvement of 6.45% and 4.31% in comparison to the EEGNet network, respectively, and effectively improves motor imagery evoked potential signals of the brain-computer interface performance.