Objective: To construct a Nomogram model for early diagnosis of Parkinson’s and validate it. Methods: This study consecutively enrolled 201 Parkinson’s patients who were hospitalized in Ruijin Hospital affiliated to Shanghai Jiaotong University between June 2013 and December 2019 as the Parkinson’s group; 201 Patients with chronic neurological diseases of non-primary Parkinson’s disease who were hospitalized during the same period served as the control group. Of the 402 cases, 300 cases (150 Parkinson’s patients and 150 non-Parkinson’s patients) were stratified and randomly selected as the training set. The remaining 102 cases (51 Parkinson’s patients and 51 non-Parkinson’s patients) were used as the validation set. In this study, R software (version 4.2.1) with complete data processing, computation, and graphing capabilities was used to analyze the data in the training set. Univariate logistic regression analysis was used to screen the risk factors for Parkinson’s disease, and multivariate logistic regression analysis and nomogram model construction were further performed. Calibration and ROC curves were used to perform internal and external validation on the training and validation sets respectively. Results: Multivariate Logistic regression analysis showed advanced age（>60） (OR=3.987 95%CI=2.126-7.477 P=0.131), cognitive dysfunction（MoCA score >26） (OR=3.094 95%CI=1.654-5.787 P<0.001), constipation (OR=2.630 95%CI=1.430-4.835 P=0.002), RBD (OR=2.710 95%CI=1.449-5.068 P=0.002), hyposmia (OR=2.117 95%CI=1.172-3.824 P=0.013), and reduced CER（<20 mg/L） (OR=3.356 95%CI=1.923-5.855 P<0.001) were risk factors for Parkinson’s. The Nomogram model was established based on the above risk factors, and the areas under the internal and external validation curves were 0.729 and 0.714, respectively. Conclusions: The Nomogram diagnostic model can effectively assist in diagnosing Parkinson’s disease and has certain clinical application value.