Vulnerability　detection　is　important　for　smart　contracts　because　of　their　immutable　and　irreversible　features.　In　this　work,　a　new　detection　method　based　on　adversarial　multi-task　learning　is　proposed　to　improve　the　accuracy　of　existing　vulnerability　detection　methods,　which　is　based　on　the　multi-task　learning　framework,　including　a　shared　part　and　a　task-specific　part.　We　optimize　the　multi-task　learning　frameworks　and　propose　the　mixed　parameter　sharing　method　to　make　each　task　not　only　maintain　its　uniqueness,　but　also　share　features　with　other　tasks,　which　helps　solve　the　problem　that　the　hard　parameter　sharing　method　cannot　constrain　the　underlying　shared　layer　and　improve　the　quality　of　extracted　features.　In　addition,　we　introduce　adversarial　transfer　learning　to　reduce　noise　pollution　caused　by　the　private　feature　and　interference　between　the　general　feature　and　the　private　feature.　We　experimented　on　datasets　obtained　from　our　previous　work,　and　the　experimental　results　prove　that　our　proposed　model　can　judge　whether　there　are　vulnerabilities　in　smart　contracts　and　then　identify　their　types.　Additionally,　the　results　also　show　that　our　model　effectively　improves　detection　accuracy　and　has　an　advantage　in　performance　over　representative　methods.