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The wind tunnel balance is a force sensor used in wind tunnel testing, which needs to be calibrated to measure the aerodynamic load on the scale model before it is used. The traditional method uses the preset polynomial function for fitting, ignoring the negative influence of some variables on the measured load, which leads to the distortion of data processing results. In this paper, an improved transit search algorithm (ITS) is proposed to select the features that are more important for load measurement. Then, Bayesian linear regression algorithm (BLR) was used to build a prediction model to measure the balance load. Finally, the method was tested on two balance datasets. The results showed that the ITS-BLR method evaluated and identified the features with higher contribution to the prediction target, thereby reducing the prediction error. The reduction is up to 60%, which shows that the proposed method can accurately predict the balance load.