To achieve automatic segmentation, quantification, and grading of different regions of leopard spots fundus (FT) using deep learning technology. The analysis includes exploring the correlation between novel quantitative indicators, leopard spot fundus grades, and various systemic and ocular parameters. This was a cross-sectional study. The data were sourced from the Beijing Eye Study, a population-based longitudinal study. In 2001, a group of individuals aged 40 and above were surveyed in five urban communities in Haidian District and three rural communities in Daxing District of Beijing. A follow-up was conducted in 2011. This study included individuals aged 50 and above who participated in the second 5-year follow-up in 2011, considering only the data from the right eye. Color fundus images centered on the macula of the right eye were input into the leopard spot segmentation model and macular detection network. Using the macular center as the origin, with inner circle diameters of 1 mm, 3 mm, and outer circle diameter of 6 mm, fine segmentation of the fundus was achieved. This allowed the calculation of the leopard spot density (FTD) and leopard spot grade for each region. Further analyses of the differences in ocular and systemic parameters among different regions’ FTD and leopard spot grades were conducted. The participants were categorized into three refractive types based on equivalent spherical power (SE): myopia (SE0.25 D). Based on axial length, the participants were divided into groups with axial length26 mm for the analysis of different types of FTD. Statistical analyses were performed using one-way analysis of variance, Kruskal-Wallis test, Bonferroni test, and Spearman correlation analysis. The study included 3 369 participants (3 369 eyes) with an average age of (63.9±10.6) years; among them, 1 886 were female (56.0%) and 1, 483 were male (64.0%). The overall FTD for all eyes was 0.060 (0.016, 0.163); inner circle FTD was 0.000 (0.000, 0.025); middle circle FTD was 0.030 (0.000, 0.130); outer circle FTD was 0.055 (0.009, 0.171). The results of the univariate analysis indicated that FTD in various regions was correlated with axial length (overall: =0.38, <0.001; inner circle: =0.31, <0.001; middle circle: =0.36, <0.001; outer circle: =0.39, <0.001), subfoveal choroidal thickness (SFCT) (overall: =-0.69, <0.001; inner circle: =-0.57, <0.001; middle circle: =-0.68, <0.001; outer circle: =-0.72, <0.001), age (overall: =0.34, <0.001; inner circle: =0.30, <0.001; middle circle: =0.31, <0.001; outer circle: =0.35, <0.001), gender (overall: =-0.11, <0.001; inner circle: =-0.04, <0.001; middle circle: =-0.07, <0.001; outer circle: =-0.11, <0.001), SE (overall: =-0.20; <0.001; inner circle: =-0.19, <0.001; middle circle: =-0.20, <0.001; outer circle: =-0.20, <0.001), uncorrected visual acuity (overall: =-0.18, <0.001; inner circle: =-0.26, <0.001; middle circle: =-0.24, <0.001; outer circle: =-0.22, <0.001), and body mass index (BMI) (overall: =-0.11, <0.001; inner circle: =-0.13, <0.001; middle circle: =-0.14, <0.001; outer circle: =-0.13, <0.001). Further multivariate analysis results indicated that different region FTD was correlated with axial length (overall: =0.020, <0.001; inner circle: =-0.022, <0.001; middle circle: =0.027, <0.001; outer circle: =0.022, <0.001), SFCT (overall: =-0.001, <0.001; inner circle: =-0.001, <0.001; middle circle: =-0.001, <0.001; outer circle: =-0.001, <0.001), and age (overall: =0.002, <0.001; inner circle: =0.001, <0.001; middle circle: =0.002, <0.001; outer circle: =0.002, <0.001). The distribution of overall (=56.76, <0.001), inner circle (=72.22, <0.001), middle circle (=75.83, <0.001), and outer circle (=70.34, <0.001) FTD differed significantly among different refractive types. The distribution of overall (=373.15, <0.001), inner circle (=367.67, <0.001), middle circle (=389.14, <0.001), and outer circle (=386.89, <0.001) FTD differed significantly among different axial length groups. Furthermore, comparing various levels of FTD with systemic and ocular parameters, significant differences were found in axial length (=142.85, <0.001) and SFCT (=530.46, <0.001). The use of deep learning technology enables automatic segmentation and quantification of different regions of theFT, as well as preliminary grading. Different region FTD is significantly correlated with axial length, SFCT, and age. Individuals with older age, myopia, and longer axial length tend to have higher FTD and more advanced FT grades.