Abstract： Objective:To construct a prediction model of vascular cognitive impairment(VCI)based on the characteristics of magnetic resonance imaging(MRI), and to verify its prediction performance.Methods:From January 2018 to January 2018, a total of 524 patients with ischemic stroke admitted to the department of Neurology of Shan County Central Hospital were followed up, 43 patients with VCI were included in the group, 20 males and 23 females, aged(63.21±8.45)years old, and the age range was 50 to 80 years old.The 43 non-VCI patients included by nearest matching method were the non-VCI Group, the non-VCI Group was 22 males and 21 females, the age was(62.38±8.47)years old, and the age range was 50 to 80 years old.All patients underwent structural MRI.The logistic regression equation was used to analyze the correlation between MRI features and VCI in patients with ischemic stroke, and a prediction model based on MRI features was constructed to analyze its value in predicting VCI.Results:There were statistically significant differences in sandardized uptake value ratio(SUVR), hippocampal volume, cortical infarction, diffuse white matter hyperintensity(WMH)volume, and vascular space enlargement between the occurrence group and the non-occurring group( P<0.05); There was no significant difference in internal volume and cerebral microhemorrhage( P>0.05). Multivariate logistic analysis showed that MRI features of SUVR( OR=8.254), hippocampus volume( OR=0.222), WMH volume( OR=4.380), vascular space enlargement( OR=7.415)are correlated with VCI in patients with ischemic stroke( P<0.05). The nomogram model shows that the C- index of the VCI prediction model based on MRI features to predict the occurrence of VCI in patients with ischemic stroke is 0.897, and the calibration curve shows that the absolute error of the prediction probability of the nomogram model is 0.040. Conclusion:MRI features of SUVR, hippocampus volume, WMH volume, and vascular space enlargement are related to the occurrence of VCI in patients with ischemic stroke.The predictive model established based on this can identify the high risk of VCI in patients with ischemic stroke.The patient provides guidance.