The　research　of　deep　convolutions　neural　network　(DNN)　in　the　depression　recognition　has　become　a　popular　topic.　In　this　paper,　we　propose　a　method　for　depression　recognition　based　on　the　regional　homogeneity　(ReHo)　in　emotional　task　state　functional　magnetic　resonance　imaging　(task-fMRI)　using　DNN.　First,　the　task-fMRI　is　extracted　by　processing　the　fMRI　of　emotional　stimulation　tasks.　And　the　task-fMRI　with　ReHo　(ReHo-task-fMRI)　is　calculated　based　on　task-fMRI.　And　then,　convolutional　networks　of　DNN　(such　as　VGG16,　etc.)　pre-trained　on　ImageNet　are　used　to　automatically　complete　extracting　the　classification　features　from　ReHo-taskfMRI.　Finally,　the　Kernel　Extreme　Learning　Machine　(KELM)　was　used　to　classify　the　depression.　The　results　of　test　set　showing　that　for　depression　recognition,　the　sensitivity　and　specificity　of　ReHo-task-fMRI　were　87.46%　and　85.35%,　however　that　of　task-fMRI　were　only　67.69%　and　55.44%.　This　study　suggest　that　compared　with　emotional　task-fMRI,　ReHo-task-fMRI　can　better　represent　the　characteristics　of　brain　dysfunction　for　patients　with　depression.