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Avian influenza is a serious zoonotic infectious disease with huge negative impacts on local poultry farming, human health and social stability. Therefore, the design of new compounds against avian influenza has been the focus in this field. In this study, computational methods were applied to investigate the compounds with neuraminidase inhibitory activity. First, 2D-SAR model was built to recognize neuraminidase inhibitors (NAIs). As a result, the accuracy of 10 cross-validation and independent tests is 96.84% and 98.97%, respectively. Then, the Topomer CoMFA model was constructed to predict the inhibitory activity and analyses molecular fields. Two models were obtained by changing the cutting methods. The second model is employed to predict the activity (q 2  = 0.784 and r 2  = 0.982). Molecular docking was also used to further analyze the binding sites between NAIs and neuraminidase from human and avian virus. As a result, it is found that same binding Total Score has some differences, but the binding sites are basically the same. At last, some potential NAIs were screened and some optimal opinions were taken. It is expected that our study can assist to study and develop new types of NAIs.