HIV-1　integrase　(IN)　has　become　an　important　target　for　antiviral　drug　discovery.　While　AIDS　drug　treatment　often　fails　due　to　the　emergence　of　drug　resistant　species.　Elvitegravir　(EVG)　is　one　of　the　FDA-approved　drugs.　We　developed　a　neural　network　prediction　model　to　make　a　qualitative　EVG　resistance　phenotype　prediction.　First,　we　developed　a　genotype-phenotype　database.　Secondly,　we　classified　the　multiple　mutations　at　the　same　site　in　three　different　ways:　mutations　result　in　the　same　volume　change,　the　same　charge　change　or　both　the　same　volume　and　charge　changes.　Finally,　we　proposed　three　neural　network　models　based　on　the　above　three　different　ways　of　classification.　The　results　show　that　the　prediction　accuracy　of　volume　model　over　the　training　set　and　test　set　are　92.2%　and　91.8%,　respectively.　The　drug　susceptibility　of　new　mutant　strains　to　EVG　can　be　predicted　using　this　model,　and　the　model　can　be　applied　as　a　diagnostic　service　for　clinicians.