To realize the three-dimensional reconstruction of space objects, the camera parameters need to be calibrated, and the calibration accuracy is the primary concern. Due to the low precision and slow convergence of traditional camera calibration method, an improved particle swarm optimization camera parameter algorithm based on dynamic adjustment and adaptive variation is proposed. The method takes the traditional calibration results as the initial value and dynamically adjusted the inertia weight of the group by defining the individual search ability, avoiding the influence of unreasonable setting of inertia weight on the algorithm search ability. In addition, the optimal particle variation is adjusted adaptively based on the degree of particle falling into local optimal, in order to improve the global search ability of the algorithm. The proposed camera parameter calibration method is compared with other calibration methods, experimental results show that the proposed algorithm has advantages.