Objective: This study aims to establish a nomogram model for predicting the risk of postoperative intracranial infection in patients with severe neurological diseases. Methods: A retrospective study was conducted on clinical data from 200 patients who underwent surgical treatment in our hospital"s neurosurgical care unit between January 2018 and January 2021. The patients were randomly divided into a training set (n=140) and a validation set (n=60). Univariate analysis and multivariate logistic regression analysis were used to screen the risk factors, constructing the prediction model by nomogram. The receiver operating characteristic (ROC) curve was plotted to assess the predictive efficacy of the nomogram model for intracranial infection in patients with severe neurological diseases. Additionally, validation of the model and evaluation of its clinical net benefit were performed using decision curve analysis (DCA). Results: The protopathy, external ventricular drainage time and lumbar cisterna drainage time were the risk factors for postoperative intracranial infection (P<0.05). ROC curve of the nomogram model showed that the AUC of the training set and the validation set were 0.774(95%CI: 0.695~ 0.853) and 0.831(95%CI: 0.725~ 0.936), respectively. DCA curves showed that the prediction of intracranial infection could improve the clinical benefit rate. Conclusion: Our nomogram prediction model based on risk factors associated with postoperative intracranial infection in patients with severe neurological diseases offers an effective approach for early identification of high-risk individuals, facilitating prompt diagnosis and treatment while improving the prognosis of patients.