Abstract: Addressing the issue of relatively large nonlinear errors in traditional calibration models for static calibration of the wind tunnel balance, researchers established a balance calibration model using the BP neural network. The BP neural network model for the three-component balance is a typical three-layer neural network, specifically manifested as a “3–7–3” structure. The precision of the BP neural network model meets the qualified criteria for static balance calibration. Its calibration performance in axial force and pitching moment components surpasses that of the traditional model, although it is slightly inferior in the normal force component. To compensate for the deficiencies of the BP neural network, an improved Butterfly Optimization Algorithm with a hybrid strategy is introduced to optimize the initial weights and thresholds. The optimized BP neural network exhibits enhanced convergence accuracy and speed. The present study utilizes the calibration data of the three-component strain gauge balance from simulation experiments, with the balance output signal values and loading load values as inputs and outputs for constructing the BP neural network. A comparison is made among between the simulation results of the traditional calibration model, the BP neural network calibration model, and the Butterfly Optimized Algorithm-BP neural network calibration model. The results indicate that the optimized BP neural network model fitting the balance calibration formula improved calibration performance by 70% – 90% compared to the traditional calibration model. It effectively eliminates the nonlinear errors of the traditional calibration model and significantly improves the precision of static balance calibration.