Current researches on camera calibration using neural network can only keep precision within a small space, unfortunabely, there has no such a method that can solve conflict between calibration precision and speed as space extends. It is proved that there are different imaging rules along three axes (XW axis, YW axis and ZW axis) in the world coordinate system, and a brand new parallel calibration method is created to calibrate camera along these three directions respectively, namely parallel calibration. A new normalization method is proposed and better calibration precisions are gained along both XW axis and YW axis. There comes conclusion that calibration along ZW axis is critical to the whole large-scale calibration since standard deviation of reconstruction along ZW axis is much higher than those along XW axis and YW axis. Expersmental results show that the new normalization method extends calibration scale while keeping both calibration precision and speed.