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Aiming at the problem of insufficient precision performance of industrial robots in the high-end manufacturing field, a quadratic identification method of kinematic parameters of industrial robots based on POE model was proposed. Firstly, the construction method of the POE kinematic error model was presented. The fitness function based on the POE kinematic error model was established for kinematics identification. Secondly, a quadratic identification method was proposed to realize the parameter identification with high precision. At first, the improved grey wolf optimizer algorithm was applied to realize the primary identification of kinematic errors. The average comprehensive position error and average comprehensive attitude error of the Staubli TX60 robot were reduced from (0.648mm,0.212°) to (0.457mm,0.166°) respectively. In order to further improve the accuracy performance of the robot, the accurate identification of kinematic errors was carried out through the LM (Levenberg-Marquard) algorithm. The average comprehensive position error and average comprehensive attitude error of the Staubli TX60 robot were reduced to (0.237 mm, 0.063°). The average comprehensive position error and average comprehensive attitude error were reduced by 63.4% and 70.2%. Finally, in order to verify the stability of the above quadratic identification method, five different sets of identification datasets and validation datasets were randomly selected for the parameter error identification of the POE error model. The results showed that the proposed quadratic identification method was able to stably and accurately identify the kinematic parameter errors of industrial robots.