Previous research has shown that participants exhibit better memory performance when consciously closing eyes (EC) compared to keeping eyes open (EO). However, the underlying dynamical mechanism remains unclear. Here, we propose a reservoir computing (RC) algorithm based on EEG-derived functional connectivity of brain networks in resting state to simulate the brain’s memory processes and reproduce the EC-related memory advantage. Our findings indicate that, compared to EO-based connectivity, the RC constructed from EC-based resting-state connectivity demonstrates superior memory performance. Further graph-theoretical analysis reveals that the EC networks exhibit stronger modularity and the modularity index are positively correlated with memory ability. Therefore, we conclude that the functional connectivity of whole brain underlies memory function and the broad reorganization of connectivity in the EC state leads to its memory advantage over the EO state.