As star sensor technology matures and accuracy requirements rise, the limiting magnitude detectable by star sensor is continuously improving, leading to a dramatic increase in the number of stars in navigation star catalogs. Therefore, to improve the recognition speed and rate of star identification, this paper proposes a star identification algorithm for star sensor suitable for high-magnitude star recognition based on the grid algorithm. By incorporating multiple calibration stars, the algorithm significantly enhances the robustness of grid pattern construction, effectively mitigating the challenges posed by noise and false stars that often hinder accurate stellar identification in grid-based algorithms. Moreover, by introducing additional constraints on angular distance and field of view, the algorithm further refines the precision of stellar recognition. Experimental results demonstrate that the algorithm can achieve all-sky star identification for stars with a limit magnitude of 10. Moreover, when subjected to a positional noise level of 2 pixels, the proposed algorithm exhibits a significantly higher success rate in stellar map recognition compared to conventional grid-based methods. In scenarios where false stars are present, the proposed algorithm consistently outperforms existing approaches, thus validating its efficacy.