摘要： The specific surface area (SSA) of ABO3-type perovskite is one of important properties associating with photocatalytic ability. In this work, data mining methods were used to explore the relationship between the SSA (ranged 1-60m2g-1) of perovskite with its features including chemical compositions and technical parameters. The genetic algorithm (GA)-support vector regression (SVR) method was used to screen the main features for modeling. The correlation coefficient (R) between predicted SSA and experimental SSA reached as high as 0.986 for training set and 0.935 for leave-one-out cross validation (LOOCV), respectively. The ABO3-type perovskite with higher SSA can be screened out by using OCPMDM (online computation platform for materials data mining) developed in our laboratory. Further, an online web server has been developed to share the model for the prediction of SSA of ABO3-type perovskite，which is accessible at the web address：http://118.25.4.79/material_api/csk856q0fulhhhwv