The　protection　dead-zone　and　threshold　setting　difficulties　of　the　residual　current　devices　(RCDs)　in　low-voltage　distribution　networks　may　lead　to　the　misidentification　of　electric　shock　fault,　resulting　in　severe　life-threatening　accidents.　This　paper　proposes　an　electric　shock　fault　identification　method　based　on　artificial　intelligence　for　RCDs.　Firstly,　Mallat　discrete　wavelet　transform　(DWT)　is　applied　to　efficiently　extract　non-stationary　electric　shock　feature　signals　from　the　total　residual　current　with　various　noises,　preventing　weak　non-stationary　electric　shock　feature　signals　from　being　filtered　out.　Based　on　the　average　and　maximum　components　of　the　signal　mutation,　an　adaptive　threshold　can　be　determined　to　detect　electric　shock　accurately,　avoiding　the　false　activation　of　RCDs　caused　by　load　fluctuations.　Subsequently,　an　autoencoder　(AE)　is　built　to　mine　the　non-linear　features　in　which　the　signal　of　electric　shock　on　living　gradually　rises　and　the　signal　of　electric　shock　on　non-living　remains　stable.　Finally,　a　back　propagation　neural　network　(BPNN)　is　trained　to　classify　the　electric　shock　types　from　the　non-linear　features.　The　simulation　and　experiment　have　been　conducted　to　obtain　total　residual　current　data　under　different　conditions,　and　the　electric　shock　fault　real-time　identification　hardware　platforms　are　developed.　The　accuracy　of　electric　shock　fault　detection　and　classification　can　reach　100　%,　which　has　advanced　its　practical　applicability.　©　2024　The　Author(s)