Abstract: Lunar Laser Ranging has extremely high requirements for the pointing accuracy of the telescopes used. To improve its pointing accuracy and solve the problem of insufficiently accurate telescope pointing correction achieved by tracking stars in the all-sky region, we propose a processing scheme to select larger-sized lunar craters near the Lunar Corner Cube Retroreflector as reference features for telescope pointing bias computation. Accurately determining the position of the craters in the images is crucial for calculating the pointing bias; therefore, we propose a method for accurately calculating the crater position based on lunar surface feature matching. This method uses matched feature points obtained from image feature matching, using a deep learning method to solve the image transformation matrix. The known position of a crater in a reference image is mapped using this matrix to calculate the crater position in the target image. We validate this method using craters near the Lunar Corner Cube Retroreflectors of Apollo 15 and Luna 17 and find that the calculated position of a crater on the target image falls on the center of the crater, even for image features with large distortion near the lunar limb. The maximum image matching error is approximately 1″, and the minimum is only 0.47″, which meets the pointing requirements of Lunar Laser Ranging. This method provides a new technical means for the high-precision pointing bias calculation of the Lunar Laser Ranging system.