At　present,　the　clinical　diagnosis　of　depression　is　mainly　through　structured　interviews　by　psychiatrists,　which　is　lack　of　objective　diagnostic　methods,　so　it　causes　the　higher　rate　of　misdiagnosis.　In　this　paper,　a　method　of　depression　recognition　based　on　SVM　and　particle　swarm　optimization　algorithm　mutation　is　proposed.　To　address　on　the　problem　that　particle　swarm　optimization　(PSO)　algorithm　easily　trap　in　local　optima,　we　propose　a　feedback　mutation　PSO　algorithm　(FBPSO)　to　balance　the　local　search　and　global　exploration　ability,　so　that　the　parameters　of　the　classification　model　is　optimal.　We　compared　different　PSO　mutation　algorithms　about　classification　accuracy　for　depression,　and　found　the　classification　accuracy　of　support　vector　machine　(SVM)　classifier　based　on　feedback　mutation　PSO　algorithm　is　the　highest.　Our　study　promotes　important　reference　value　for　establishing　auxiliary　diagnostic　used　in　depression　recognition　of　clinical　diagnosis.