Objective: To establish a nomograph model for prediction of cervical central lymph node metastasis (CLNM) among patients with thyroid papillary carcinoma (PTC), so as to provide the evidence for designing personalized treatment plans for PTC. Methods : The data of patients that underwent thyroidectomy and were pathologically diagnosed with PTC post-surgery in the Affiliated Traditional Chinese Medicine Hospital of Xinjiang Medical University from 2018 to 2021 were collected. Patients' data captured from 2018 to 2020 and from 2021 were used as the training set and the validation set, respectively. Predictive factors were screened using a multivariable logistic regression model, and the nomograph model for prediction of CLNM risk was established. The predictive value of the model was evaluated using the receiver operating characteristic (ROC) curve and the adjusted curve. Results: Totally 1 820 PTC cases were included in the training set, including 458 cases with CLNM (25.16%), and 797 cases in the validation set, including 207 cases with CLNM (25.98%). The prediction model is p=ey/(1+ey), y=0.761 + 0.525 × sex + (-0.039) ×age + 0.351 × extrathyroid invasion + 0.368 × neck lymph node enlargement + 1.021×maximum tumor diameter + (-0.009) × TT4 + (-0.001) × anti-TPOAb. The area under the ROC curve was 0.732 for the training set and 0.731 for the validation set, and Hosmer-Lemeshow test showed a good fitting effect (P=0.936, 0.722). Conclusion The nomograph model constructed in this study has a high predictive value for CLNM among patients with PTC.