Detecting　high-impedance　faults　(HIFs)　in　distribution　networks　poses　a　significant　challenge　for　conventional　relay　devices　due　to　low　fault　currents　and　various　characteristics　such　as　weaker　fault　features,　distortion　offset,　and　background　noise　interference.　This　article　introduces　a　novel　and　streamlined　method　for　HIF　detection,　which　ingeniously　integrates　frequency-band　energy　curve　(FBEC)　analysis　and　Gaussian　smoothing　to　extract　trend　changes,　thus　enhancing　the　precision　and　effectiveness　of　HIF　detection.　The　proposed　method　utilizes　continuous　wavelet　transform　(CWT)　to　extract　the　time-frequency　spectrum　from　the　zero-sequence　current.　By　analyzing　the　feature-band　energy,　the　FBEC　is　computed.　To　mitigate　noise　interference　and　enhance　the　periodic　change　pattern,　a　Gaussian　filter　is　applied　for　smoothing.　Distinguishing　between　HIF　and　normal　operations,　including　low　impedance　fault　(LIF),　capacitor　switching　(CS),　inrush　current　(IC),　and　ferromagnetic　resonance　(FR),　is　achieved　by　analyzing　the　peak　points　of　FBEC.　The　proposed　method＇s　performance　was　extensively　validated　through　simulations　and　field　data.　The　performance　of　the　proposed　method　was　extensively　validated　through　a　series　of　simulations　and　detailed　analysis　of　real-world　field　data.　The　results　demonstrated　an　excellent　detection　performance　on　field　data,　with　an　impressive　accuracy　rate　of　86.5%　and　an　F-1　-score　of　0.87.　Moreover,　we　examined　the　method＇s　resilience　against　noise　using　data　with　a　signal-to-noise　ratio　(SNR)　of　20　dB,　resulting　in　a　detection　accuracy　of　77.3%　and　an　F-1　-score　of　0.79.　These　findings　underscore　the　method＇s　clear　physical　meaning,　strong　interpretability,　and　versatility,　establishing　its　effectiveness　and　practicality　for　real-world　applications.