Up　to　now,　there　is　still　the　absence　of　research　about　depression　recognition　using　resting-state　functional　magnetic　resonance　imaging　(rest_fMRI)　and　deep　learning.　Previous　studies　have　shown　that　regional　homogeneity　(ReHo)　of　rest_fMRI　(rest_ReHo_fMRI)　is　a　characterization　of　the　functional　synchronization　of　adjacent　voxels　in　brain　regions,　and　the　mental　and　behavioral　abnormalities　in　depression　are　due　to　an　imbalance　of　ReHo　synchronization　in　some　brain　functional　areas.　Accordingly,　this　paper　presents　a　method　for　depression　recognition　using　rest_ReHo_fMRI.　First,　the　rest_ReHo_fMRI　is　extracted　from　the　preprocessed　rest-fMRI　by　calculation.　Then,　deep　convolutional　networks　(such　as　VGG16)　pretrained　on　ImageNet　are　used　to　automatically　complete　extracting　the　classification　features　from　rest_ReHo_fMRI.　Finally,　the　Kernel　Extreme　Learning　Machine　(KELM)　was　used　to　classify　the　depression.　The　results　of　the　test　set　show　that　the　proposed　method　achieves　89.07%　in　sensitivity　and　89.74%　in　specificity.　This　study　suggests　that　features　of　rest_ReHo_fMRI　can　be　used　as　biomarkers　to　distinguish　depression　from　normal　people.