Brain　networks　classification　is　an　important　subject　in　brain　science.　In　recent　years,　brain　networks　classification　based　on　convolutional　neural　networks　has　become　a　hot　topic.　However,　it　is　still　difficult　to　accurately　classify　brain　network　data　with　high　dimension　and　small　sample　size.　Due　to　the　close　relationship　between　different　clinical　phenotypes　and　brain　networks　of　different　populations,　it　is　highly　possible　to　provide　auxiliary　information　for　the　brain　networks　classification.　Therefore,　we　propose　a　new　brain　networks　classification　method　based　on　an　adaptive　multi-task　convolutional　neural　network　in　this　paper.　Firstly,　the　clinical　phenotype　predictions　are　introduced　as　different　auxiliary　tasks　and　the　shared　representation　mechanism　of　multi-task　convolutional　neural　networks　is　used　to　provide　general　and　useful　information　for　brain　networks　classification.　Then,　in　order　to　reduce　the　experimental　cost　and　the　error　caused　by　the　manual　operation,　a　new　adaptive　method　is　proposed　to　substitute　for　manual　adjustments　of　the　weight　of　every　task　in　the　multi-task　learning.　The　experimental　results　on　the　autism　brain　imaging　data　exchange　I　(ABIDE　I)　dataset　show　that　the　multi-task　convolutional　neural　networks　which　introduce　clinical　phenotype　predictions　can　achieve　better　classification　results.　Moreover,　the　adaptive　multi-task　learning　method　can　further　improve　the　performance　of　brain　networks　classification.　©　2020,　Science　Press.　All　right　reserved.