Objective: To investigate the construction of a predictive model for estimation of central cervical lymph node metastasis of papillary thyroid microcarcinoma (PTMC) based on the ultrasonographic features of the nodules and clinical data of the patients. Methods: The clinical data of 121 patients with PTMC that was confirmed by surgical and pathological findings and their ultrasonic images of 166 thyroid nodules were collected. The relevant factors for central cervical lymph node metastasis were analyzed and picked up by statistical methods, and then, the Logistic and Additive scoring models for estimating risk of central cervical lymph node metastasis were established, respectively. Results: The results of statistical analyses showed that age, multifocal lesion, nodule size, relationship between nodule and thyroid capsule, and imaging feature of perinodular enhancement were closely related to the risk of central cervical lymph node metastasis (all P<0.05). These factors were numerically assigned and scored. According to the total score evaluated by the area under the ROC curve (AUC), when the score derived from the Logistic model reached 6.5, the AUC was 0.964 with a sensitivity of 98.4% and a specificity of 74.1%; when the score derived from Additive model reached 33.5, the AUC was 0.928 with a sensitivity of 88.9% and a specificity of 84.5%. Hosmer-Lemeshow goodness of fit test indicated that Logistic model had a better fit. Conclusion: The constructed Logistic model has certain predictive value for estimating risk of central cervical lymph node metastasis in PTMC, and it can provide a quantitative basis for the treatment plan selection of PTMC. When its score is equal to or greater than 7, the possibility of central cervical lymph node metastasis should be highly suspected.