Our AdsMT demonstrates excellent performance in predicting GMAE, with mean absolute errors (MAE) below 0.15 eV for two of the datasets. A transfer learning strategy was also proposed to further improve AdsMT's performance on small-sized datasets. Moreover, crossattention weights are exploited to identify the most energetically favorable adsorption sites and demonstrate the intrinsic interpretability of AdsMT. The calibrated uncertainty estimation is integrated into our AdsMT for reliable GMAE prediction. Overall, AdsMT exhibits strong learning ability, generalizability, and interpretability, making it a powerful tool for fast GMAE calculations and catalyst screening.