摘要Influenza caused by influenza virus,seriously threaten human life and health.Drug treatment is one of the effective measurement. However, there are only two classes of drugs, one class is M2 blockers and another is neuraminidase (NA)inhibitors. The recent antiviral surveillance studies reported a global significant increase in M2 blocker resistance among influenza viruses, and the resistant virus strains against NA inhibitor are also reported in clinical treatment.Therefore thediscovery of new medicines with low resistance has become very urgent.As all known,traditional medicines with multi-target features and network mechanism often possess low resistance. Compound Yizhihao, which consists of radix isatidis,folium isatidis,Artemisia rupestris,is one of the famous traditional medicine for influenza treatment in China, however its mechanism of action against influenza is unclear. In this study, the multiple targets related with influenza disease and the known chemical constituents from Compound Yizhihao were collected, and multi-target QSAR (mt-QSAR) classification models were developed by Na?ve Bayesian algorithm and verified by various datasets. Then the classification models were applied to predict the effective constituents and their drug targets.Finally,the constituent-target-pathway network was constructed,which revealed the effective constituents and their network mechanism in Compound Yizhihao. This study will lay important basis for the clinical uses for influenza treatment and for the further research and development of the effective constituents.