In the process of data sampling and fitting reconstruction of optical system point spread function, the background noise and external interference will cause the error measurement results of instrument. The traditional fixed objective function point spread function fitting algorithm exhibits poor adaptability, making it difficult to accurately restore the morphology of light spots during optical instrument testing. These error terms will affect the analysis results of image quality to some extent. To solve this problem, a calculation method of point spread function for optical imaging system is proposed. The energy acquisition and reconstruction are carried out according to different spot morphology, and the original shape of spot is preserved effectively. Through the cubic spline interpolation of spot image data, the subpixel matrix is constructed. By performing scattered light suppression and centroid correction on different positions of light spots, we can obtain a three-dimensional surface closer to the real spot shape. Considering sensor accuracy variations, the method adjusts step size to control the spot radius corresponding to encircled energy, accommodating different testing requirements. Gaussian spot simulation analysis and laboratory test results show that, compared with Gaussian fitting method, nearest neighbor interpolation method and blind deconvolution method, the method proposed in this paper is closer to the actual situation. The error of algorithm based on surface interpolation is only, ε=0.000 2, the deviation rate within the observation area of image quality is less than 5%. This method can provide more accurate capability concentration results. The algorithm is sensitive to the morphological changes of the point spread function and can effectively distinguish the presence of multiple peaks in small size spot. It also has high accuracy under normal temperature test conditions. The algorithm has engineering applications in the field of interference source location and optical focusing, and provides a theoretical foundation for the development and performance analysis of optical imaging systems.