The　key　issue　in　the　field　of　smart　contract　security　is　efficient　and　rapid　vulnerability　detection　in　smart　contracts.　Most　of　the　existing　detection　methods　can　only　detect　the　presence　of　vulnerabilities　in　the　contract　and　can　hardly　identify　their　type.　Furthermore,　they　have　poor　scalability.　To　resolve　these　issues,　in　this　study,　we　developed　a　smart　contract　vulnerability　detection　model　based　on　multi-task　learning.　By　setting　auxiliary　tasks　to　learn　more　directional　vulnerability　features,　the　detection　capability　of　the　model　was　improved　to　realize　the　detection　and　recognition　of　vulnerabilities.　The　model　is　based　on　a　hard-sharing　design,　which　consists　of　two　parts.　First,　the　bottom　sharing　layer　is　mainly　used　to　learn　the　semantic　information　of　the　input　contract.　The　text　representation　is　first　transformed　into　a　new　vector　by　word　and　positional　embedding,　and　then　the　neural　network,　based　on　an　attention　mechanism,　is　used　to　learn　and　extract　the　feature　vector　of　the　contract.　Second,　the　task-specific　layer　is　mainly　employed　to　realize　the　functions　of　each　task.　A　classical　convolutional　neural　network　was　used　to　construct　a　classification　model　for　each　task　that　learns　and　extracts　features　from　the　shared　layer　for　training　to　achieve　their　respective　task　objectives.　The　experimental　results　show　that　the　model　can　better　identify　the　types　of　vulnerabilities　after　adding　the　auxiliary　vulnerability　detection　task.　This　model　realizes　the　detection　of　vulnerabilities　and　recognizes　three　types　of　vulnerabilities.　The　multi-task　model　was　observed　to　perform　better　and　is　less　expensive　than　a　single-task　model　in　terms　of　time,　computation,　and　storage.