The invention provides a Kawasaki disease classification and prediction method based on medical data modeling. The method comprises the following steps: S1, data sample selection: extracting a valid sample which can be modeled from a sample data set; S2, feature sieving: sieving 19 features conforming to field medical auxiliary diagnosis application from a feature set for constructing sample data for modeling; S3, Kawasaki disease classifying model construction and evaluation: fitting an Xtrain data set on a training set by using a random forest classifying method, recording optimal modeling parameters and weights of all selected features, and classifying and predicting a test set sample according to the classifying model. In the Kawasaki disease classification and prediction method, Kawasaki disease relevant data are analyzed and modelled systemically, and an evaluation method for model prediction is provided, so that the model can be used for performing effective auxiliary diagnosis on a Kawasaki disease of a patient based on Kawasaki disease data, effective prevention and intervention and treatment are performed at the early stage of attacking, and a basis is laid for a best treatment effect.