Carotid artery stenosis, Carotid Intima Media Thickness (CIMT) or carotid artery plaque may lead to stroke. For large-scale preliminary screening of stroke, an improved Variational AutoEncoder (VAE) based on medical data was proposed to predict and identify abnormal carotid arteries. Firstly, for the missing values in medical data, K-Nearest Neighbor ( KNN), Mixture of mean, mode and KNN (M KNN) method and improved VAE were respectively used to impute the missed values to obtain the complete dataset, improving the application range of the data. Secondly, the feature attributes were analyzed and the features were ranked in order of importance. Thirdly, four supervised algorithms, Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting Tree (XGBT), were combined with Genetic Algorithm (GA) to build the abnormal carotid artery identification models. Finally, based on the improved VAE, a semi-supervised abnormal carotid artery prediction model was built. Compared to the performance of baseline model, the performance of the semi-supervised model based on the improved VAE improves significantly with sensitivity of 0.893 8, specificity of 0.927 2, F1-measure of 0.910 5 and classification accuracy of 0.910 5. Experimental results show that this semi-supervised model can be used to identify the abnormal carotid arteries and thus serves as a tool to recognize high-risk groups of stroke, preventing and reducing the occurrence of stroke.