The　existing　voltage　sag　source　localization　only　utilizes　one　simulation　type　or　measurement　data　based　on　sampled　waveform　data.　Moreover,　it　requires　more　storage　space　and　transmission　channels　and　cannot　combine　the　advantages　of　simulated　and　measurement　data,　resulting　in　poor　applicability　of　the　model.　Hence,　this　paper　proposes　a　voltage　sag　source　locating　method　based　on　transfer　learning　from　the　sag　information　in　the　sag　event　list　combined　with　the　grid　structure　data.　Firstly,　the　location　features　of　the　sag　source　are　extracted　from　the　degree　of　sag　impact,　network　structure,　and　sag-type　information　based　on　the　characteristics　of　simulated　and　measurement　data　that　characterize　the　position　of　the　sag　source,　and　they　are　collectively　used　as　inputs　to　the　model.　Then,　the　simulated　data　is　used　to　build　a　multi-classification　model　based　on　the　multi-layer　perceptron　with　the　line　number　as　the　classification　number,　and　the　measurement　data　is　employed　to　fine-tune　the　model　parameters　to　achieve　transfer　learning.　Finally,　voltage　sag　source　localization　is　achieved　based　on　the　trained　multi-classification　model.　The　correctness　of　the　proposed　method　in　this　paper　is　verified　through　simulation　and　actual　measurement　in　a　specific　area　of　East　China.