Nowadays,　smart　monitoring　devices　such　as　digital　fault　indicator　(DFI)　have　been　installed　in　distribution　systems　to　provide　sufficient　information　for　fault　location.　However,　it　is　still　a　challenge　to　extract　effective　features　from　massive　data　for　single-line-to-ground　(SLG)　fault-section　location.　This　work　proposes　a　novel　method　of　fault-section　location　using　a　1-D　convolutional　neural　network　(1-D　CNN)　and　waveform　concatenation.　After　SLG　fault　occurs,　DFI　measures　the　transient　zero-sequence　currents　at　double-ends　of　the　line　section,　which　could　be　concatenated　to　construct　characteristic　waveform.　The　features　of　characteristic　waveforms　would　be　extracted　adaptively　by　1-D　CNN　to　locate　the　fault　section.　Furthermore,　the　problem　where　the　on-site　recorded　data　are　hard　to　collect　would　be　solved　because　1-D　CNN　only　needs　a　small　number　of　samples　for　training　in　practical　applications.　The　experimental　results　verified　that　the　proposed　method　could　work　effectively　under　various　fault　conditions,　even　if　a　few　DFIs　are　out　of　order.