Since the existing telescope pointing error correction model cannot satisfy the needs of laser ranging systems for accurately detecting space debris, a back propagation neural network model optimized by genetic and Levenberg-Marquardt algorithms is proposed for correcting the telescope pointing error. A total of 102 stars are observed in the station's hemispherical sky area, which are divided into zones covering equal azimuth and altitude angle intervals. These observation data are used for modeling. Additionally, 12 stars are observed the following day to evaluate the model's accuracy, and the results show that its accuracies in terms of azimuth and pitch are 1.94″ and 1.12″, respectively. Finally, the proposed model is used in experiments to detect space debris and cooperative space targets. Results show that, when we use the laser ranging technique, the telescope's orientation accuracies in terms of azimuth and pitch for space debris are 1.89″ and 1.21″ respectively, and that for cooperative space targets are 2.06″ and 1.46″ respectively, showing a significant improvement over the traditional model. These results are also significant from the viewpoint of improving the detection success rate of space debris.