The rupture of vulnerable plaques is the principal cause of luminal thrombosis in acute ischemic stroke. The identification of plaque features that indicate risk for disruption may predict cerebrovascular events. Here, we aimed to build a high-risk intracranial plaque model that differentiates symptomatic from asymptomatic plaques using radiomic features based on high-resolution magnetic resonance imaging (HRMRI). One hundred and seventy-two patients with 188 intracranial atherosclerotic plaques (100 symptomatic and 88 asymptomatic) with available HRMRI data were recruited. Clinical characteristics and conventional plaque features on HRMRI were measured, including high signal on T1-weighted images (HST1), the degree of stenosis, normalized wall index, remodeling index, and enhancement ratio (ER). Univariate and multivariate analyses were performed to build a traditional model to differentiate between symptomatic and asymptomatic plaques. Radiomic features were extracted from pre-contrast and post-contrast HRMRI. A radiomic model based on HRMRI was constructed using random forests, ridge, least absolute shrinkage and selection operator, and deep learning (DL). A MIX model was constructed based on the radiomic model and the traditional model. Gender, HST1, and ER were associated with symptomatic plaques and were included in the traditional model, which had an area under the curve (AUC) of 0.697 in the training set and 0.704 in the test set. The radiomic model achieved an AUC of 0.982 in the training set and 0.867 in the test dataset for identifying symptomatic plaques. In the training set, the MIX model showed an AUC of 0.977. In the test set, the MIX model exhibited an improved AUC of 0.895, which outperformed the traditional model (p = 0.032). Radiomic analysis based on DL and machine learning can accurately identify high-risk intracranial plaques.