Along　with　the　wide　application　of　intelligent　systems　in　various　fields,　the　combination　of　data　fusion　and　knowledge　graph　has　become　the　key　to　enhance　the　system’s　problem　solving　capability.　However,　existing　data　fusion　methods　still　face　challenges　when　dealing　with　multi-source　heterogeneous　data,　especially　in　how　to　effectively　combine　knowledge　graph.　Therefore,　this　paper　systematically　reviews　existing　data　fusion　methods　based　on　knowledge　graph　and　classifies　them　into　three　categories:　fusion　of　raw　data,　fusion　of　raw　data　with　knowledge　graph,　and　fusion　of　knowledge　graphs.　Each　category　of　methods　is　described　and　analyzed　in　detail　by　combining　a　general　framework　with　specific　examples.　In　addition,　this　paper　also　discusses　the　future　research　direction　of　data　fusion　based　on　knowledge　graph,　and　analyzes　the　challenges　and　opportunities　it　faces.　This　paper　provides　a　theoretical　framework　and　practical　guidance　for　the　problem　of　multi-source　heterogeneous　data　fusion,　and　provides　methodological　support　for　the　development　of　intelligent　systems.　©　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature　2025.