Abstract: To improve patients’ health, decrease unplanned hospital readmission rate, alleviate patients’ burden and prevent social resources waste, an unplanned hospital readmission risk prediction model was built, utilizing machine learning method and based on a dataset collected from a regional health care information platform of China. Different from existing works which only predict readmission risk, this research tried to model the problem from a multi-class classification view and predict readmission time and probability simultaneously. 10 classifiers were built by adjusting the parameters of neural network, random forest and support vector machine. Experiments on real dataset showed that the support vector machine classifier using polynomial kernel function performed best in terms of prediction accuracy, which was about 96.96%. The research result can assess readmission risk more precisely in time and probability based on patients’ historical health care data. With the help of the result, medical agencies can adopt proper interventions and reduce unplanned hospital readmission rate.