RSE is important for understanding cyclic molecule reactivity. However, due to the high computational expense of QM, efficiently and accurately computing RSE remains a challenge. We developed the AIMNet2 RSE workflow and a GNN model to predict RSE accurately and efficiently. These calculations have been validated both computationally and experimentally. In a benchmark study, the AIMNet2 RSE workflow achieved an MAE of around 1 kcal/mol and an R² of 0.997 with respect to the accurate ωB97M-D4/Def2-TZVPP calculations.