Objective To develop and validate a nomogram by non-invasive method to assess the risk of post-stroke depression （PSD） based on National Health and Nutrition Examination Survey （NHANES）database， to provide a valuable reference for early clinical screening of high-risk individuals. Methods A total of 1 003 stroke survivors surveyed from 2007 to 2018 were selected from the NHANES database for the study. A total of 659 cases from 2007 to 2014 were assigned to development group， and 344 cases from 2015 to 2018 were assigned to validation group. Depressive symptoms in stroke survivors were assessed by the Patient Health Questionnaire-9 （PHQ-9）. Multivariate Logistic regression was applied to analyze the influencing factors of PSD in stroke survivors. Predictive factors with P ＜ 0.10 in multivariate Logistic regression analysis was incorporated into the nomogram. The prediction performance of nomogram was evaluated by area under curve （AUC） of receiver operator characteristic （ROC） curve. The clinical application value of nomograms was analyzed by decision curve. Results Of all the 1 003 patients， there were 190 cases （18.94%） were assessed with depression symptoms （the score of PHQ-9 greater or equal to 10）， with 124 cases in development group （18.82%） and 66 cases in validation group （19.19%）. Multivariate Logistic analysis showed that female （OR=1.671， 95%CI=1.040-2.684）， sleep disorder （OR=2.797， 95%CI=1.740-4.494）， work limitation （OR=2.293， 95%CI=1.362-3.861） and difficulty walking （OR=2.163， 95%CI=1.304-3.588） were independent risk factors for PSD in stroke survivors （P＜ 0.05）， and 60 to 79 years old （OR=0.321， 95%CI=0.121-0.852），≥ 80 years old （OR=0.117， 95%CI=0.032-0.426） were the protective factor for PSD in stroke survivors （P ＜ 0.05）. The nomogram was constructed based on gender， age， history of cardiovascular disease， sleep disorders， work restrictions and walking disorders. The AUC was 0.797 （95%CI=0.756-0.838） in the development group. After 1 000 times internal validation with bootstrap resampling methods， the C-index was 0.782. The AUC was 0.752 （95%CI=0.684-0.820） in the validation group. The calibration curve showed that the predicted probability of PSD by the nomogram was basically consistent with the actual probability of occurrence. The decision curve results showed that when the threshold probability was between 5% and 75%， using this predictive model to screen stroke patients would result in higher net benefits. Conclusions The nomogram of PSD constructed has a good predictive performance， which can be used for early PSD risk screening in stroke patients to help physicians make better treatment decisions.