Camera calibration is an important premise for accurate positioning in robot machine vision systems. To solve the problem of low accuracy of traditional camera calibration, this paper proposes a camera calibration optimization method based on an improved particle swarm optimization algorithm. This method uses Zhang Zhengyou calibration method to obtain the initial value of camera intrinsic parameters and realizes the nonlinear self-adaptive adjustment of inertial weight parameters in different iteration stages, balancing the local and global search capabilities. Dynamic self-adjusting strategies of sines and cosines changes in different iteration stages are adopted for global and local learning factors to further improve the global search ability further and late search accuracy. When a particle swarm is about to fall into the local optimum, the dispersing mechanism is used to enlarge the spatial range of the particle swarm to avoid premature convergence of the algorithm. Experimental results show that the proposed method has better precision and repeatability as compared with the traditional methods.