The triaxial test of discrete elements in rockfill material has the problem of excessive influence factors and time-consuming for mesoscopic parameter calibration. On the basis of summarizing and analysing the current rockfill mesoscopic models, a mesoscopic parameter calibration model based on the quantum genetic algorithm(QGA)and support vector machine(SVM)was established. The Latin hypercube sampling was used to generate the mesoscopic parameter groups, and then the stress-strain curves were calculated by the discrete element method. In order to simulate the complex nonlinear relationship between the mesoscopic parameters and the stress-strain curves, the QGA was used to train SVM to achieve the best learning effect. According to the indoor triaxial test results and taking advantage of the speed of SVM, the mesoscopic parameters of rockfill were calibrated by the QGA searching process. The calibration example of rockfill shows that QGA-SVM can quickly and accurately calibrate the mesoscopic parameters of the discrete elements, indicating a good application value in practical engineering.