Fault　localization　is　crucial　for　ensuring　stability,　particularly　in　high　impedance　faults　(HIF)　characterized　by　low　current　levels　and　prolonged　transient　processes　(TP).　Existing　methods　predominantly　analyze　differences　in　the　fixed-length　transient　waveform,　potentially　causing　delays　in　triggering　or　failure　in　HIF　scenarios.　To　address　these　challenges,　a　novel　AI　application　paradigm　for　HIF　localization　was　introduced,　incorporating　both　adaptive　TP　calibration　and　multiscale　correlation　analysis.　Based　on　1D-Unet,　the　TP　of　the　zero-sequence　voltage　(ZSV)　can　be　adaptively　calibrated　to　maximize　the　utilization　of　transient　information.　Subsequently,　the　differential　zero-sequence　voltage　(DZSV)　and　transient　zero-sequence　current　(TZSC)　can　be　acquired　to　facilitate　multiscale　correlation　analysis.　Combined　with　a　sliding　window　strategy,　the　micro　correlation　between　DZSV　and　TZSC　is　articulated　through　the　local　correlation　degree　(LCD).　The　comprehensive　correlation　degree　(CCD)　between　DZSV　and　TZSC　is　then　formulated　to　realize　fault　feeder/　section　localization　at　the　macro　level.　The　1D-Unet　model　achieved　a　classification　accuracy　of　99.2　%　for　sample　points　in　test　datasets　and　showed　robustness　with　an　accuracy　exceeding　93.5　%　in　the　presence　of　20　dB　noise　interference.　When　integrated　with　the　well-trained　1D-Unet,　the　proposed　approach　underwent　further　validation　using　simulation　data　and　field　recordings.　These　tests　confirmed　the　model＇s　resilience　to　noise　interference　up　to　20　dB　and　its　efficacy　across　networks　of　diverse　topologies,　such　as　the　IEEE-13　and　34-node　distribution　networks.　Additionally,　an　industrial　prototype　applying　this　framework　identified　all　fault　conditions　without　false　positives　or　omissions,　outperforming　existing　methods　under　various　fault　scenarios,　including　those　involving　high　impedance　materials　and　different　resistance　levels　across　multiple　feeders.