Abstract: Objective: To explore the risk factors of pregnancy complicated with craniocerebral diseases and construct a predictive Nomogram model. Methods: The clinical data of 230 pregnant women and parturients who were admitted to Beijing Tiantan Hospital affiliated to Capital Medical University from June 2020 to July 2023were included in the study. According to the presence or absence of craniocerebral disease, they were divided into two groups: craniocerebral disease group (n=48) and non-craniocerebral disease group (n=182). Logistic regression was used to analyze the independent influencing factors of pregnancy complicated with craniocerebral diseases, and they were included in the construction of a Nomogram prediction model, and the concordance index(C-index), receiver operating characteristic(ROC) curve and calibration curve in R software were used to evaluate the efficacy of Nomogram model for pregnancy complicated with craniocerebral diseases. Results: There were no significant differences in age, gestational week, gestational frequency, cesarean section history, miscarriage history, stroke history, hemoglobin(Hb), platelet count(PLT), activated partial thrombin time(APTT), prothrombin time(PT), and low-density lipoprotein(LDL-C) between the two groups (all P＞0.05). The history of hypertension, number of deliveries, and progesterone in the craniocerebral disease group were higher than those in the non-craniocerebral disease group, while the white blood cell count(WBC) and fibrinogen(FIB) were lower than those in the non-craniocerebral disease group (P＜0.05). Logistic regression analysis showed that history of hypertension, number of deliveries, WBC, and FIB were all independent risk factors for pregnancy complicated with craniocerebral diseases(OR＞1, P＜0.05); the results of ROC curves showed that the history of hyper-tension, number of deliveries, WBC, and area under the ROC curve(AUC) value of FIB were all greater than 0.700. Based on the above influencing factors, a Nomogram risk model was established. The calibration curve Cindex value was 0.769 and the AUC values of the ROC curve modeling group and validation group were 0.897and 0.872, respectively. Conclusion: The Nomogram prediction model based on the independent risk factors of pregnancy complicated with craniocerebral diseases can intuitively predict the risk factors of pregnancy complicated with craniocerebral diseases.