High-impedance　fault　(HIF)　detection　has　always　been　difficult　in　distribution　networks　due　to　the　lack　of　field　data　and　the　large　difference　between　field　and　simulation　waveforms.　Based　on　the　characteristics　of　zero-sequence　currents,　a　novel　HIF　detection　methodology　is　proposed,　which　combines　time-frequency　spectrum　(TFS)　and　transfer　convolutional　neural　network　(TCNN).　First,　the　TFSs　are　acquired　by　applying　continuous　wavelet　transform　(CWT)　to　the　collected　zero-sequence　currents.　Then,　the　TFSs　of　simulated　zero-sequence　currents　are　utilized　for　training　source-domain　convolutional　neural　network　(SCNN).　Next,　the　SCNN　is　transfer　learned　with　very　few　TFSs　of　field　zero-sequence　currents　to　obtain　TCNN.　The　performance　of　the　proposed　method　is　verified　by　simulation　samples　and　field　samples.　The　results　show　that　the　proposed　method　can　effectively　extract　fault　features　from　small-scale　training　samples　under　different　fault　circumstances.　Besides,　TCNN　can　adaptively　extract　the　effective　features　of　field　HIF　and　detect　field　HIF　more　accurately　than　SCNN.　Finally,　this　article　provides　a　visualization　scheme　for　interpretability　of　the　neural　network,　which　offers　visual　explanations　for　the　decision-making　basis　of　the　neural　network.