The nycterohemeral star survey in short-wave infrared band is expected to realize the all-time automatic navigation in near-earth space,in which the star map recognition algorithm is one of the key technologies for realizing the all-time navigation.The traditional triangle algorithm is prone to redundant matching and miss-matching in recognition due to its low dimension of matching features when the number of navigation stars increases.To address this problem,this paper proposes a dual-feature based shortwave infrared star map recognition algorithm,which selects dual high-dimensional features of triangles area and tangent circle radius as matching features,and reduces the computational complexity of matching recognition by constructing a K-vector index of area features and applying K-vector lookup method.In addition, an optimized selection strategy of observation triangles is proposed to reduce the computation cost in the matching process and improve the recognition speed of the algorithm.The test shows that:1) The recognition rate is higher than 95% when the noise of star point position is less than 2 pixels;and 2) The recognition rate of the algorithm can reach 87.6% when the number of pseudo-stars does not exceed 50%.The feasibility of the proposed algorithm is verified by actual star observation test.Compared with the improved triangle algorithm,the algorithm has obvious advantages in the recognition speed,recognition rate and noise resistance capability.