Effective　features　are　essential　for　fault　diagnosis.　Due　to　the　faint　characteristics　of　a　single　line-to-ground　(SLG)　fault,　fault　line　detection　has　become　a　challenge　in　resonant　grounding　distribution　systems.　This　paper　proposes　a　novel　fault　line　detection　method　using　waveform　fusion　and　one-dimensional　convolutional　neural　networks　(1-D　CNN).　After　an　SLG　fault　occurs,　the　first-half　waves　of　zero-sequence　currents　are　collected　and　superimposed　with　each　other　to　achieve　waveform　fusion.　The　compelling　feature　of　fused　waveforms　is　extracted　by　1-D　CNN　to　determine　whether　the　fused　waveform　source　contains　the　fault　line.　Then,　the　1-D　CNN　output　is　used　to　update　the　value　of　the　counter　in　order　to　identify　the　fault　line.　Given　the　lack　of　fault　data　in　existing　distribution　systems,　the　proposed　method　only　needs　a　small　quantity　of　data　for　model　training　and　fault　line　detection.　In　addition,　the　proposed　method　owns　fault-tolerant　performance.　Even　if　a　few　samples　are　misjudged,　the　fault　line　can　still　be　detected　correctly　based　on　the　full　output　results　of　1-D　CNN.　Experimental　results　verified　that　the　proposed　method　can　work　effectively　under　various　fault　conditions.