Acoustic　emission　(AE)　source　localization　is　a　powerful　detection　method.　Time　Difference　Mapping　(TDM)　method　is　an　effective　method　for　detecting　defects　in　complex　structures.　The　core　of　this　method　is　to　search　for　a　point　with　the　minimum　distance　away　from　the　verification　point　in　the　time　difference　database.　In　Traditional　Time　Difference　Mapping　(T-TDM)　method　and　Improved　Time　Difference　Mapping　(I-TDM)　method,　the　larger　database　and　denser　grids　allow　the　higher　localization　accuracy.　If　the　location　points　are　not　included　in　the　database,　the　localization　accuracy　of　the　T-TDM　and　I-TDM　methods　will　be　greatly　affected.　To　solve　the　above　problems,　a　new　AE　source　localization　method,　Generalized　Regression　Neural　Network　Based　on　Time　Difference　Mapping　(GRNN-TDM),　is　proposed　to　improve　the　localization　accuracy　in　the　study.　In　the　proposed　method,　the　time　difference　data　of　the　sensor　path　on　all　nodes　in　the　time　difference　mapping　are　used　as　the　training　input　data　and　the　coordinates　of　grid　nodes　are　used　as　the　training　output　data.　After　continuous　learning　and　training,　the　neural　network　model　predicts　its　possible　source　location　with　the　time　difference　data　collected　from　the　verification　point.　In　this　paper,　the　localization　of　AE　sources　with　T-TDM,　I-TDM　and　GRNN-TDM　methods　was　studied　in　four　composite　plates　with　different　fiber　layers　and　an　aluminum　plate　with　holes.　The　localization　results　showed　that　the　localization　accuracy　of　the　GRNN-TDM　method　was　higher　than　that　of　T-TDM　and　I-TDM　methods.