A motor imagery brain-computer interface system with practical application value should be able to show stable performance when facing new users. The distribution of electrodes on the cerebral cortex is the same for any user. Therefore, in order to solve the subject-independent problem, we propose a novel Graph Convolutional Convolution Transformer Net (GCCTN), which uses a graph convolutional neural network to calculate the relationship between an electrode and other electrodes, uses a convolutional neural network to extract temporal and spatial information and uses a Transformer Encoder for further extraction of time-domain information. Finally, the classification accuracy of our model is optimal.