Accurate　fault　classification　is　the　premise　of　fault　location　and　management　study　in　a　power　distribution　network.　In　most　of　the　traditional　fault　classification　methods　used　in　power　distribution　network,　the　characteristic　quantities　are　selected　by　experience,　which　will　increase　the　uncertainty　of　fault　classification　results.　A　novel　fault　classification　method　based　on　deep　belief　networks　(DBN)　is　proposed　in　this　paper.　Samples　of　fault　current　and　voltage　are　preprocessed　by　min–max　standardization　and　waveform　splicing　firstly,　then　they　are　used　to　train　the　DBN　together　with　fault　type　label.　Characteristic　quantities　of　the　current　and　voltage　will　be　automatically　extracted　by　the　well-trained　DBN　model,　and　the　reliable　fault　type　classification　of　distribution　network　can　be　realized.　Simulation　and　experimental　results　show　that　the　fault　classification　method　is　suitable　for　distribution　network,　and　it　has　not　only　characteristics　of　obvious　fault　feature　extraction　and　high　fault　classification　accuracy,　but　also　has　good　adaptability　while　the　neutral　grounding　modes　changing　or　used　in　power　distribution　network　with　distributed　generator.　©　2020　Institute　of　Electrical　Engineers　of　Japan.　Published　by　Wiley　Periodicals　LLC.　©　2020　Institute　of　Electrical　Engineers　of　Japan.　Published　by　Wiley　Periodicals　LLC.