Named　entity　linking　is　a　process　of　linking　a　given　reference　in　a　document　to　a　knowledge　base.　In　natural　language　processing,　entity　linking　can　enhance　the　computer＇s　understanding　of　unstructured　text　data.　Applying　traditional　entity　linking　methods,　especially　entity　linking　methods　for　person　names　and　organization　names,　has　its　limitations.　Similar　vocabulary　as　an　entity　to　be　linked　is　difficult　to　make　full　use　of　its　contextual　semantic　information　for　ambiguity　elimination.　This　paper　makes　full　use　of　the　entity＇s　category　attribute　and　the　semantic　information　contained　in　the　context　to　design　an　entity　linking　method　based　on　entity　category　and　semantic　word　embedding.　First,　Training　text　classification　model　based　on　corpus　to　obtain　entity　attributes.　Then　the　semantic　feature　is　extracted　by　the　word　vector　template　to　perform　entity　disambiguation　through　the　semantic　classification　model.　Finally,　the　results　of　the　entity　linking　are　predicted　by　means　of　model　ensemble.　Experiments　show　that　the　accuracy　of　the　method　after　fusion　on　the　entity　linking　dataset　has　improved.　©　2019　Published　under　licence　by　IOP　Publishing　Ltd.