Although intracranial hemorrhage in moyamoya disease can occur repeatedly, predicting the disease is difficult. Deep learning algorithmsdeveloped in recent years provide a new angle for identifying hidden risk factors, evaluating the weight of different factors, and quantitatively evaluating the risk of intracranial hemorrhage in moyamoya disease. To investigate whether convolutional neural network algorithms can be used to recognize moyamoya disease and predict hemorrhagic episodes, we retrospectively selected 460 adult unilateral hemispheres with moyamoya vasculopathy as positive samples for diagnosis modeling, including 418 hemispheres with moyamoya disease and 42 hemispheres with moyamoya syndromes. Another 500 hemispheres with normal vessel appearance were selected as negative samples. We used deepresidual neural network (ResNet-152) algorithms to extract features from raw data obtained from digital subtraction angiography of theinternal carotid artery, then trained and validated the model. The accuracy, sensitivity, and specificity of the model in identifying unilateralmoyamoya vasculopathy were 97.64 ± 0.87％, 96.55 ± 3.44％, and 98.29 ± 0.98％, respectively. The area under the receiver operatingcharacteristic curve was 0.990. We used a combined multi-view conventional neural network algorithm to integrate age, sex, and hemorrhagic factors with features of the digital subtraction angiography. The accuracy of the model in predicting unilateral hemorrhagic risk was 90.69± 1.58％ and the sensitivity and specificity were 94.12 ± 2.75％ and 89.86 ± 3.64％, respectively. The deep learning algorithms we proposed were valuable and might assist in the automatic diagnosis of moyamoya disease and timely recognition of the risk for re-hemorrhage. Thisstudy was approved by the Institutional Review Board of Huashan Hospital, Fudan University, China (approved No. 2014-278) on January 12, 2015.