Due　to　the　complexity　of　integrated　circuit　design　and　manufacturing　process,　an　increasing　number　of　third　parties　are　outsourcing　their　untrusted　intellectual　property　(IP)　cores　to　pursue　greater　economic　benefits,　which　may　embed　numerous　security　issues.　The　covert　nature　of　hardware　Trojans　(HTs)　poses　a　significant　threat　to　cyberspace,　and　they　may　lead　to　catastrophic　consequences　for　the　national　economy　and　personal　privacy.　To　deal　with　HTs　well,　it　is　not　enough　to　just　detect　whether　they　are　included,　like　the　existing　studies.　Same　as　malware,　identifying　the　attack　intentions　of　HTs,　that　is,　analyzing　the　functions　they　implement,　is　of　great　scientific　significance　for　the　prevention　and　control　of　HTs.　Based　on　the　fined　detection,　for　the　first　time,　this　article　proposes　a　two-stage　Graph　Neural　Network　model　for　HTs＇　multifunctional　classification,　GNN4HT.　In　the　first　stage,　GNN4HT　localizes　HTs,　achieving　a　notable　true　positive　rate　(TPR)　of　94.28%　on　the　Trust-Hub　dataset　and　maintaining　high　performance　on　the　TRTC-IC　dataset.　GNN4HT　further　transforms　the　localization　results　into　HT　information　graphs　(HTIGs),　representing　the　functional　interaction　graphs　of　HTs.　In　the　second　stage,　the　dataset　is　augmented　through　logical　equivalence　for　training　and　HT　functionalities　are　classified　based　on　the　extracted　HTIG　from　the　first　stage.　For　the　multifunctional　classification　of　HTs,　the　correct　classification　rate　reached　as　high　as　80.95%　at　gate-level　and　62.96%　at　register　transfer　level.　This　article　marks　a　breakthrough　in　HT　detection,　and　it　is　the　first　to　address　the　multifunctional　classification　issue,　holding　significant　practical　importance　and　application　prospects.