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Objective To conduct an intelligent auxiliary diagnosis model of influenza in children（Flu A and Flu B） was conducted thought machine learning algorithm, so as to assist in the pre-diagnosis of infectious diseases. Methods Taking the children with respiratory tract infectious who were in the outpatient clinic from Jan 2013 to Jun 2020 as the research object, the basic characteristics information, nasopharyngeal swabs, and routine blood test data were included, and Python was used for data processing and statistical analysis. Then, based on the machine learning algorithm Logistics regression model and the GBDT model to construct an auxiliary diagnostic model and calculate the eigenvalues.The indicators such as ROC, AUC value and model probability prediction box plot were used as the criteria to judge the performance of models. Results Among the scope of the study, nasopharyngeal swabs showed that 38 094 cases were positive for Flu A infection, 24 792 cases were positive for Flu B infection, and 215 cases were positive for combined Flu A with Flu B infection, totaling 63 101 cases.Twenty-five indicators were included as the model characteristic values.The AUC values of Flu A auxiliary diagnosis model based on Logistics model and GBDT model were 0.877 and 0.884, respectively, and the first five crucial characteristics were age, percentage of monocytes, white blood cells, lymphocytes absolute value and Creactive protein. The AUC values of Flu B auxiliary diagnosis were 0.895 and 0.902, and the top five important characteristics were age, percentage of monocytes, eosinophilic cell count, white blood cells and platelets. The effects of GBDT model are better than that of Logistics model, and GBDT model has the best performance in the differential diagnosis of positive cases of single Flu B infection （AUC=0.902）. Conclusion In this study, an intelligent auxiliary diagnosis model of Flu A and Flu B in children based on blood routine test was established, which could accurately identify positive patient with Flu A and Flu B from the patient with respiratory tract infectious diseases before diagnosis. With good migration, it could play a role on the auxiliary diagnosis before diagnosis in practical application scenarios. [ABSTRACT FROM AUTHOR]