In　recent　years,　the　brain　functional　connection　classification　method　based　on　deep　learning　has　become　a　hot　research　topic.　In　order　to　further　improve　the　classification　accuracy　of　brain　functional　connections　and　gain　discriminative　features　associated　with　a　disease,　we　propose　a　brain　functional　connection　classification　method　based　on　prototype　learning　and　deep　feature　fusion　in　this　paper.　Firstly,　we　use　stacked　autoencoders　to　extract　lower-to-higher　deep　features　from　brain　functional　connections.　Then　the　prototype　learning　is　used　to　extract　the　distance　features　of　the　sample　category　from　each　hidden　layer　of　the　stacked　autoencoders.　Finally,　the　deep　feature　fusion　strategy　is　adopted　to　fuse　these　distance　features　and　the　fused　features　are　applied　for　the　brain　functional　connection　classification.　The　experimental　results　on　the　ABIDE　dataset　show　that　compared　with　other　methods,　the　proposed　method　not　only　has　a　higher　classification　accuracy,　but　also　can　locate　the　brain　areas　related　to　the　disease　more　precisely.　Copyright　©2019　Acta　Automatica　Sinica.　All　rights　reserved.