Abstract: Objective To investigate the risk factors affecting the short-term prognosis of patients with aneurysmal subarachnoid hemorrhage (aSAH) after surgery and to construct a nomogram model for prognosis prediction. Methods A total of 341 patients who were diagnosed with aSAH and treated surgically at the Affiliated Hospital of Xuzhou Medical University from January 2019 to January 2021 were enrolled and their clinical data, imaging results, and laboratory indicators were collected for retrospective analysis. The patients were randomly divided into a training set and a validation set at a ratio of 7∶3. Univariate and multivariate logistic regression analyses were used to identify factors influencing the short-term prognosis of aSAH patients. A nomogram model was constructed and validated. Results Multivariate logistic regression analysis indicated that age, modified Fisher grading, Hunt-Hess grading, and CT evidence of intraventricular hemorrhage were independent risk factors for the postoperative 3-month prognosis of aSAH patients. The receiver operating characteristic (ROC) curve demonstrated a sensitivity of 0.85, a specificity of 0.87, and an area under the curve (AUC) of 0.901 in the training set, while the validation set showed a sensitivity of 0.86, a specificity of 0.88, and an AUC of 0.922. Calibration curves indicated good consistency between predicted probabilities and actual outcomes. Decision curve analysis further confirmed that the model had good clinical benefit. Conclusions The nomogram model constructed in this study can effectively predict the short-term prognosis of aSAH patients.