Objective To construct and validate the prediction model of early neurological deterioration （END） in patients with branch atheromatous disease （BAD）. Methods From January 2017 to December 2022， 246 END patients with BAD who were admitted to the Department of Neurology of Yellow River Sanmenxia Hospital were selected as participants. All patients were randomly divided into a modeling group （n=172） and a validation group （n=74） according to a 7∶3 ratio. General and clinical information of patients were collected. Multivariate Logistic regression was used to analyze the influencing factors of END in patients with BAD. Consistency index and receiver operating characteristic （ROC） curve were used to analyze the discriminative power of the predictive model in the modeling and validation groups， and a calibration curve was drawn to evaluate the accuracy of the predictive model in two groups. Results Among the patients in the modeling group， there were 109 males （63.37%） and 63 females （36.63%）， with an age of （65.47±13.00） years and a median onset time of 4.00 （2.00， 7.00） hours. Among the patients in the validation group， there were 39 males （52.70%） and 35 females （47.30%）， with an age of （66.09±12.14） years and a median onset time of 2.50 （1.90， 6.00） hours. Multivariate Logistic regression showed that smoking， degree of white matter lesions， neutrophil count， low-density lipoprotein cholesterol， and intravenous thrombolysis was the influencing factors for END in patients with BAD， and the differences were statistically significant （P＜ 0.05）. The sensitivity and specificity of the prediction model were 82.29% and 72.37%， respectively. The sensitivity and specificity of the validation model were 73.17% and 72.73%， respectively. The calibration curves were close to the curve with a straight line of 1. Conclusions Based on smoking， white matter lesion degree， neutrophil count， low-density lipoprotein cholesterol， intravenous thrombolysis， the prediction model of END in patients with BAD established by nomogram can assist in predicting the short-term prognosis of patients.