Understanding the structure–activity relationship of surface lattice oxygen is critical but challenging to design efficient redox catalysts. This paper describes data‐driven redox activity descriptors on doped vanadium oxides combining density functional theory and interpretable machine learning. We corroborate that the p‐band center is the most crucial feature for the activity. Besides, some features from the coordination environment, including unoccupied d‐band center, s‐ and d‐band fillings, also play important roles in tuning the oxygen activity. Further analysis reveals that data‐driven descriptors could decode more information about electron transfer during the redox process. Based on the descriptors, we report that atomic Re‐ and W‐doping could inhibit over‐oxidation in the chemical looping oxidative dehydrogenation of propane, which is verified by subsequent experiments and calculations. This work sheds light on the structure–activity relationship of lattice oxygen for the rational design of redox catalysts. Data‐driven interpretable descriptors were constructed to predict the redox activity of surface lattice oxygen on doped vanadium oxides by combining density functional theory (DFT) and machine learning (ML). Based on descriptors, physical insights into the structure–activity relationships were obtained and further guided the experimental verification of efficient redox catalysts for chemical looping oxidative dehydrogenation (CL‐ODH) of propane.