This work provides a graph network solution for pilot brain fatigue state inference based on electroencephalography (EEG) fatigue indicators. Two graph methods are built as follows. The first one uses a single EEG signal sample as a node, and fatigue detection as a node classification task in a graph network. The developed graph network is then utilized to extract the correlation among different samples to achieve multisample joint decision making. The second method uses a single EEG signal sample as a graph structure, and EEG fatigue prediction as a graph classification task. Electrode position correlation is used to construct a graph. The feature fusion of adjacent electrodes is obtained through the connection relationship among nodes in a graph structure to improve network learning accuracy. In addition, a Bayesian optimization method is proposed to model the randomness of attention weights, and a Bayesian graph attention network is built. This work constructs a based-graph deep learning structures to achieve a pilot fatigue detection model with high accuracy, good generalization, and strong adaptability. Experimental results demonstrate the effectiveness of the proposed model.