Driving fatigue has become a serious hidden danger to road traffic safety. Drivers in a fatigued state often have problems such as delayed reactions and lack of concentration, which increases the risk of traffic accidents. In fatigued driving, the brain activity of the driver undergoes a series of changes, such as a decrease in the frequency of brain waves and a decrease in the amplitude of electroencephalogram (EEG) signals. Therefore, we propose a novel Semi-supervised Label Propagation with Optimal Graph Learning (SOGL) model that for identifying the fatigue state of drivers. This model uses class information from a small amount of labeled EEG data to assist the learning of unlabeled data and uses soft projection matrix learning to handle non-linear data structures. In addition, we also introduce a partially labeled graph learning method that extracts potential data structure information through graph structure learning techniques to improve the robustness and generalization ability of the model. Experimental results show that the model has good performance on a driving fatigue dataset.