Objective：To construct a model for predicting pelvic lymph node metastasis in early - stage cervical squamous cell carcinoma. Methods：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. A univariate logistic analysis was performed based on the pathological examination results of pelvic lymph nodes as the outcome indicator. Two models were developed by univariate logistic analysis as well as 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 optimal model was then 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 decision curve analysis（DCA）. Bootstrap method was used 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，respectively. According to the results of AIC，serial NRI and IDI，model 2 was the optimal model [indicators：age，squamous cell carcinoma antigen（SCCA），carbohydrate antigen（CA）125 and lymph node status on magnetic resonance imaging]. The AUC was 0.818；the Hosmer-Lemeshow test results were χ² =0.942 and P=0.332，and the adjusted AUC was 0.800. DCA results showed that when the threshold probability was 0.03-0.50，the clinical net benefit was relatively high. The AUC of the internal verification of Bootstrap was 0.784. Conclusion：Models based on age，SCCA，CA125，and lymph node status in magnetic resonance imaging can help predict pelvic lymph node status for cervical cancer before surgery.