In　order　to　address　the　challenges　posed　by　weak　and　variable　high-impedance　fault　signals　and　limited　data　availability　in　practical　distribution　networks,　a　novel　method　for　detecting　high-impedance　faults　is　proposed.　Initially,　a　multi-head　variational　autoencoder　model　based　on　squeeze-excitation　networks　is　employed　to　augment　the　small　sample　dataset.　Subsequently,　the　data　are　filtered,　and　the　temporal　and　frequency　domain　features　are　extracted,　respectively.　Considering　the　weak　characteristics　of　high　impedance　fault　features　and　the　limitations　of　the　proliferation　model　in　generating　comprehensive　and　effective　fault　features,　a　categorical　boosting　algorithm　based　on　the　gradient　harmonized　mechanism　(GHM-CatBoost)　is　introduced.　The　GHM-CatBoost　algorithm　incorporates　a　gradient　harmonized　mechanism　loss　function　to　address　the　imbalance　in　attention　between　easily　distinguishable　and　challenging　samples,　thereby　mitigating　the　issue　of　overfitting.　The　research　findings　suggest　that　the　data　proliferation　model　can　produce　fault　samples　with　a　blend　of　simulation　data　diversity　and　measured　data　randomness,　thereby　enhancing　the　richness　of　the　dataset.　Furthermore,　the　fault　recognition　accuracy　achieved　by　the　proposed　GHM-CatBoost　model　is　notably　high　at　97.21%,　outperforming　its　counterpart　classifier　model.　Moreover,　the　efficacy　of　the　proposed　approach　is　validated　through　rigorous　testing　and　comparative　analysis.　©　2025　Science　Press.　All　rights　reserved.