Most　of　the　existing　research　on　argumentation　mining　is　focused　on　modeling　single　dataset,　and　the　possible　changes　in　feature　of　different　datasets　are　neglected.　And　thus　the　generalization　performance　of　the　model　is　decreased.　Therefore,　an　argumentation　mining　method　based　on　multi-task　learning　is　proposed　to　combine　the　argumentation　mining　tasks　of　multiple　datasets　for　joint　learning.　Firstly,　the　input　layers　of　multiple　tasks　are　fused,　and　the　sharing　parameters　of　word　level　and　character　level　are　obtained　via　deep　convolutional　neural　network　and　highway　network.　The　joint　task-related　feature　input　into　stacking　long-short　term　memory　is　utilized　to　train　the　correlation　information　between　multiple　argumentation　mining　tasks　in　parallel.　Finally,　the　results　of　sequence　labeling　are　obtained　by　the　conditional　random　field.　The　experimental　results　on　six　datasets　of　various　fields　verify　the　effectiveness　of　the　proposed　method　with　increased　Macro-F1.　©　2019,　Science　Press.　All　right　reserved.