Objective To construct a predictive model for the risk of intravenous medication using machine learning algorithms based on the clinical information of patients. Methods A retrospective analysis was performed for the patients receiving intravenous medication，with 1302 patients in the modeling group and 281 in the validation group. The drug-related problem classification system V9.09 proposed by the European Society of Clinical Pharmacy was used to analyze the drug-related problems in patients. Machine learning algorithms，including logistic regression，neural network，CHAID decision tree，Bayesian network，and support vector machine，were used to construct risk predictive models for intravenous medication，and confusion matrices were used to evaluate the performance of each predictive model. Accuracy，recall rate，precision，and the area under the ROC curve（AUC） for the subjects in the validation group were used to evaluate the predictive performance of the model. Results The incidence rate of drug-related problems was 26.9% among these patients. These drug-related problems mainly involved treatment safety（n=556，94.9%），followed by treatment effectiveness（n=30，5.1%）. Among the models constructed，support vector machine algorithm showed the best predictive performance，with an AUC of 0.826. Conclusion The predictive model for the risk of intravenous medication constructed using machine learning algorithms has good predictive performance，which can provide new insights and methods for the management of intravenous medication safety.