Aiming at the problem of poor adaptability and applicability of traditional error compensation methods for air defense radar, a Back Propagation (BP) neural network optimized based on improved particle swarm optimization algorithm is constructed, which can estimate radar error more stably and accurately and compensate for radar measurements, so as to better improve radar detection accuracy.Firstly, the convergence factor and dynamic adaptive adjustment of inertia weight are introduced to improve the global optimization ability and convergence speed of particle swarm optimization algorithm.Secondly, the improved particle swarm optimization algorithm is used to optimize the initial weight and threshold of BP neural network, improve the estimation accuracy of BP neural network and reduce the training time.The actual measurement data of a certain radar is used for simulation and verification.The simulation results show that the accuracy and error fluctuation of the range, azimuth and pitch angle after compensation are greatly improved.Compared with the traditional method, the compensation effect is better, and the engineering applicability and popularization applicability are stronger.