Hard　exudates　are　important　manifestations　and　diagnostic　bases　for　diabetic　retinopathy.　To　solve　the　problem　of　easy　disturbance　of　hard　exudates　detection　by　image　background　and　noise,　a　hard　exudates　clustering　detection　method　based　on　neighborhood　constraint　model　is　proposed.　Firstly,　the　detection　area　is　set.　The　target　detection　function,　which　is　defined　by　the　gray　and　spatial　information　of　pixels,　is　used　to　complete　the　clustering　segmentation　of　the　image　by　iterative　calculation.　Then　the　gray　differences　of　the　neighborhood　are　calculated,　and　the　greatest　gray　change　is　used　as　the　constraint　condition　of　the　similarity　decision　to　determine　whether　each　cluster　image　is　hard　exudates.　The　performance　of　the　method　is　verified　on　the　open　eye　image　databases.　The　results　show　that　the　method　can　effectively　identify　and　detect　the　possible　hard　exudation　in　the　fundus　image.　The　accuracy　of　the　normal　image　reaches　90%,　and　the　sensitivity　and　positive　predictive　value　for　hard　exudates　achieve　79%　and　81%,　respectively.　The　method　is　thus　proved　conducive　to　the　computer-aided　diagnosis　of　the　fundus　diseases.　©　2018,　Beijing　China　Science　Journal　Publishing　Co.　Ltd.　All　right　reserved.