Surging interests exist in double-atom catalysts (DACs), which not only inherit the advantages of single-atom catalysts (SACs) (e.g., ultimate atomic utilization, high activity, and selectivity) but also overcome the drawbacks of SACs (e.g., low loading and isolated active site). The design of DACs, however, remains cost-prohibitive for both experimental and computational studies, due to their huge design space. Herein, by means of density functional theory (DFT) and topological information-based machine-learning (ML) algorithms, we present a data-driven high-throughput design principle to evaluate the stability and activity of 16 767 DACs for oxygen evolution (OER) and oxygen reduction (ORR) reactions. The rational design reveals 511 types of DACs with OER activity superior to IrO2 (110), 855 types of DACs with ORR activity superior to Pt (111), and 248 bifunctional DACs with high catalytic performance for both OER and ORR. An intrinsic descriptor is revealed to correlate the catalytic activity of a DAC with the electronic structures of the DAC and its bonding carbon surface structure. This data-driven high-throughput approach not only yields remarkable prediction precision (>0.926 R-squared) but also enables a notable 144 000-fold reduction of screening time compared with pure DFT calculations, holding promise to drastically accelerate the design of high-performance DACs.