Inverse kinematics is a general method for defining the joint angles of the robot arm. This method provides an efficient way to control the robot arm for several tasks. However, the server motors or the mechanism design of the robot arm may not always be ideal. If the motor consumption is existed, the error of the final position of the robot arm will be increased. In order to solve this problem, this paper proposes an intelligent method for the posture calibration of the robot arm. In this paper, the particle swarm optimization (PSO) algorithm and the proposed neural network model are integrated to calibrate the kinematics of the robot arm. The experimental results show that the control error can be reduced by applying the proposed method. The feasibility and practicality of the proposed method are also validated in the experiments.