Many　sensors　like　digital　fault　indicators　(DFIs)　have　been　applied　and　promoted　in　distribution　systems.　The　sensors　can　provide　a　technical　mean　for　single-line-to-ground　(SLG)　fault　section　location,　but　there　are　still　some　feature　extraction　and　fault　diagnosis　problems.　A　novel　SLG　fault　section　location　method　utilizing　auto-encoder　(AE)　and　fuzzy　C-means　(FCM)　clustering　is　presented　in　this　work.　Taking　advantage　of　abundant　information　provided　by　DFIs,　striking　features　can　be　extracted　by　the　AE　network,　which　is　different　from　the　artificially　designed　features　that　rely　on　prior　knowledge.　Compared　with　the　learning-based　methods　requiring　massive　training　data,　the　proposed　method　only　requires　the　data　from　one　SLG　fault.　By　applying　the　AE　network　to　the　zero-sequence　current　measured　by　DFIs,　the　SLG　fault　section　location＇s　striking　features　could　be　obtained.　Through　feature　classification　by　FCM　clustering　without　setting　threshold,　the　positional　relationship　between　each　detection　node　and　the　fault　point　would　be　distinguished　to　locate　the　fault　section.　Considering　the　abnormal　communication　of　DFIs,　the　experiment　proves　that　the　proposed　method　can　work　effectively　under　various　fault　conditions.