A　method　based　on　Double　Tree　Complex　Wavelet　Transform　(DTCWT)-Deep　Belief　Network　(DBN)　for　overvoltage　identification　in　distribution　network　is　proposed.　The　three-phase　overvoltage　signal　of　the　10　kV　bus　is　subjected　to　the　dual-tree　complex　wavelet　transform,　and　then　the　singular　value　is　reduced　by　the　singular　value　decomposition.　The　resulting　singular　value　is　input　into　the　trained　deep　belief　network　classifier　as　the　eigenvalue,　and　the　seven　typical　internal　overvoltage　type　identification　types　are　realized.　The　proposed　algorithm　is　trained　and　tested　using　ATP/EMTP　simulation　data　and　fault　waveforms　on　the　physics　experiment　platform,　and　compared　with　other　classification　algorithms.　The　results　show　that　compared　with　other　methods　listed　in　this　paper,　the　proposed　algorithm　has　stronger　feature　extraction　capability　and　higher　recognition　accuracy.　©　2019,　Power　System　Protection　and　Control　Press.　All　right　reserved.