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Aiming at the problem of large fitting error of manual calibration data for FDR sensors, the data from other regions were introduced as auxiliary data, and an automatic calibration model based on migration learning was established. In this model, historical data from other regions were introduced as auxiliary data. Data collected from FDR targets were used as source data. Combined with auxiliary data and a small amount of source data, an accurate FDR sensor calibration model can be obtained by using TrAdaBoost algorithm. TrAdaBoost algorithm for classification problem was improved to TrAdaBoost algorithm for regression. The basic learner of TrAdaBoost algorithm was changed from AdaBoost to XGBoost, which improved the calculation method of error rate when updating weight. Firstly, XGBoost was used to train the auxiliary data to get the initial calibration model, and then a small amount of data was collected from the target location of FDR, and the improved TrAdaBoost algorithm was used to calibrate the initial calibration model, so that the accurate FDR calibration model can be obtained. The data of 10 different regional sites were trained as auxiliary data to obtain the initial calibration model. For the six sites in Shenyang, the target sites were used respectively. Totally 80% of the data were used as the source domain data for model correction, and the remaining 20% were used for testing. The results showed that the average preparation rate using the calibration method was 99.1%, which indicated that the automatic calibration model using migration learning was effective and accurate. 