To improve the identification accuracy of the static model of quartz flexible accelerometer, this study proposes a static model parameter identification method based on a Particle Swarm Optimization?Back Propagation (PSO?BP) neural network. This approach addresses the local optima susceptibility of Back Propagation (BP) neural networks through Particle Swarm Optimization (PSO) integration. The neural architecture is configured according to accelerometer input?output dimensions, where the PSO's global exploration capability optimizes the initial weight for the BP network. Precision centrifuge?based calibration experiments were conducted to validate the proposed method. Experimental results demonstrate that the PSO?BP neural network exhibits significantly enhanced capability in resolving nonlinear coefficients compared to the standard BP network, achieving a reduction of the mean squared error (MSE) by two orders of magnitude, which provides technical support for advancing the development of high?precision navigation technologies in airborne inertial navigation systems.