In　the　existing　knowledge　completion　models　with　textual　or　neighbor　information,　the　interaction　between　texts　and　neighbors　is　ignored.　Therefore,　it　is　difficult　to　capture　the　information　with　strong　semantic　relevance　to　entities.　In　addition,　the　relationship-specific　information　in　the　entities　is　not　taken　into　account　in　the　models　based　on　convolutional　neural　networks,　which　results　in　poor　prediction　performance.　In　this　paper,　a　circular　convolutional　neural　network　model　based　on　triplet　attention　is　proposed　combining　textual　and　neighbor　information.　Firstly,　the　words　with　strong　semantic　relevance　to　entities　in　textual　descriptions　are　selected　by　semantic　matching,　and　then　they　are　combined　with　topological　neighbors　as　entity　neighbors　to　enhance　entity　representations.　Next,　the　fusion　representations　of　the　entity　and　the　relation　representations　are　reshaped.　Finally,　the　triplet　attention　is　utilized　to　optimize　the　input　of　the　convolution　and　the　convolution　operation　can　extract　the　features　related　to　the　relations　in　the　entities,　which　improves　the　model　performance.　Experiments　on　several　public　datasets　show　that　the　performance　of　the　proposed　model　is　superior.　©　2022,　Science　Press.　All　right　reserved.