To develop a highly efficient and fully automated method that measures retinal vessel caliber using digital retinal photographs and evaluate the association between retinal vessel caliber and hypertension.The subjects of this study were from two sources in Beijing, China, a hypertension case-control study from Tongren Hospital (Tongren study) and a community-based atherosclerosis cohort from Peking University First Hospital (Shougang study). Retinal vessel segmentation and arteriovenous classification were achieved simultaneously by a customized deep learning model. Two experienced ophthalmologists evaluated whether retinal vessels were correctly segmented and classified. The ratio of incorrectly segmented and classified retinal vessels was used to measure the accuracy of the model's recognition. Central retinal artery equivalents, central retinal vein equivalents and arteriolar-to-venular diameter ratio were computed to analyze the association between retinal vessel caliber and the risk of hypertension. The association was then compared to that derived from the widely used semi-automated software (Integrative Vessel Analysis).The deep learning model achieved an arterial recognition error rate of 1.26%, a vein recognition error rate of 0.79%, and a total error rate of 1.03%. Central retinal artery equivalents and arteriolar-to-venular diameter ratio measured by both Integrative Vessel Analysis and deep learning methods were inversely associated with the odds of hypertension in both Tongren and Shougang studies. The comparisons of areas under the receiver operating characteristic curves from the proposed deep learning method and Integrative Vessel Analysis were all not significantly different (p > .05).The proposed deep learning method showed a comparable diagnostic value to Integrative Vessel Analysis software. Compared with semi-automatic software, our deep learning model has significant advantage in efficiency and can be applied to population screening and risk evaluation.