Pavement state sensor is an important tool for qualitative identification and quantitative measurement of pavement state, and its quantitative measurement performance depends on the accuracy of quantitative calibration model. In order to solve the problem of nonlinearity and nonuniform distribution of quantitative calibration data of pavement state sensor, a quantitative calibration model of pavement state sensor based on particle swarm optimization-adaptive support vector regression (PSO-ASVR) was proposed. Firstly, the adaptive preprocessing (AP) process was constructed to optimize the pre-processing of the calibration data to reduce the calibration data processing error under the influence of the nonuniform distribution of the pavement state sensor. Then, the support vector regression (SVR) algorithm based on structural risk minimization was used to fit the calibration data, and the particle swarm optimization (PSO) algorithm was used to realize the parameter optimization in the SVR to reduce the data fitting error introduced by the nonlinear calibration data of the pavement state sensor. Experiments on calibration data processing under different pavement states show that the root-mean-square error (RMSE) of the new method can be reduced by at least 63% compared with that of the traditional method, which verifies the effectiveness of the new method in improving the accuracy of the quantitative calibration model and realizes the reduction of the quantitative calibration error of the pavement state sensor.