Objective: To construct a model for predicting pelvic lymph node metastasis in early-stage cervical squamous cell carcinoma. Method:The clinical data of patients in the Department of Gynecology, Affiliated Hospital of Southwest Medical University from January 2019 to October 2023 were retrospectively analyzed. Two models were obtained by univariate logistic analysis , forward and backward stepwise regression analysis. The Akaike Information Criterion (AIC), Continuous Net Reclassification Improvement Index (NRI) and Integrated Discriminant Improvement Index (IDI) were used to determine the optimal model .The model was converted into a nomogram, and its efficacy was evaluated by the area under the receiver operating characteristic curve(AUC), Hosmer-Lemeshow test, calibration curve and clinical decision curve (DCA). Bootstrap self-samples 1000 times for internal validation. Results: A total of 221 patients were enrolled. Forward and backward stepwise regression methods were used to establish model 1 and model 2. According to the results of AIC, serial NRI and IDI, model 2 was the optimal model (indicators: age, SCCA, CA125, and lymph node status on MRI). The AUC was 0.818 ; The Hosmer-Lemeshow test χ2=0.942, P=0.332, and the adjusted AUC was 0.800 ; DCA showed that when the threshold probability was 0.03-0.5, the clinical net benefit was higher. The AUC of the internal verification was 0.784. Conclusion:Models based on age, SCCA, CA125, and MRI lymph node status can help predict pelvic lymph node status for cervical cancer before surgery.