Mental fatigue has attracted much attention from researchers as it plays a key role in performance efficiency and safety situations. Functional connectivity analysis using graph theory is an effective method for revealing changes in cognition resources influenced by mental fatigue. Previous studies have revealed that functional networks are dynamically reorganized. Therefore, it is critical to explore dynamic timescales of networks related to specific cognitive abilities. In this study, we used an open EEG dataset of twenty-one subjects recorded in a 60-minutes sustained attention task. After preprocessing, we constructed connectivity matrices using the weighted phase lag index (wPLI) in the theta band and characterized them with dynamic graph measures, namely characteristic path length (CPL) and clustering coefficient (CC). The results show that the frontal-parietal brain networks in theta band are involved in a sustaining attention task. When averaging from temporal and spatial activations, CPL and CC decreased with time-on-task. Our results indicate that mental fatigue results in deteriorations in sustaining attention, and graph theory analysis can provide support for mental fatigue analysis.Clinical Relevance- Identification of the effects of long term sustained attention on dynamic brain networks may be potential for mechanism study and detection of mental states and attentional deficits caused by mental diseases.