Antioxidant peptides (AOPs) hold great promise for mitigating oxidative-stress-related diseases, but their discovery is hindered by inefficient and time-consuming traditional methods. To address this, we developed an innovative framework combining machine learning and quantum chemistry to accelerate AOP identification and analyze structure-activity relationships. A Bi-LSTM-based model, AOPP, achieved superior performance with accuracies of 0.9043 and 0.9267, precisions of 0.9767 and 0.9848, and Matthews correlation coefficients (MCCs) of 0.818 and 0.859 on two data sets, outperforming existing methods. Compared with XGBoost and LightGBM, AOPP demonstrated a 4.67% improvement in accuracy. Feature fusion significantly enhanced classification, as validated by UMAP visualization. Experimental validation of ten peptides confirmed the antioxidant activity, with LLA exhibiting the highest DPPH and ABTS scavenging rates (0.108 and 0.437 mmol/g, respectively). Quantum chemical calculations identified LLA's lowest HOMO-LUMO gap (ΔE = 0.26 eV) and C3-H26 as the key active site contributing to its superior antioxidant potential. This study highlights the synergy of machine learning and quantum chemistry, offering an efficient framework for AOP discovery with broad applications in therapeutics and functional foods.