High-frequency　magnetic　components　are　widely　used　in　various　equipment　in　power　electronics.　The　loss　characteristics　of　magnetic　core　components　are　key　parameters　that　significantly　affect　their　performance,　with　higher　losses　leading　to　lower　converter　efficiency.　To　improve　the　accuracy　of　core　loss　prediction,　we　proposed　a　magnetic　core　loss　prediction　method　based　on　multi-feature　dimensionality　reduction　and　an　XGBoost　five-fold　cross-validation　model.　Key　features　such　as　material,　frequency,　temperature,　waveform　type,　peak　magnetic　flux　density,　and　effective　magnetic　flux　density　are　analyzed　and　identified.　Multi-feature　dimensionality　reduction　is　performed　using　Pearson　correlation　analysis,　principal　component　analysis,　and　SelectKBest,　resulting　in　three　selected　input　features.　A　fivefold　cross-validation　prediction　model　based　on　XGBoost　is　constructed,　and　core　loss　prediction　research　is　conducted　using　the　obtained　sample　data.　The　results　show　that　the　proposed　method　achieves　a　mean　absolute　percentage　error　(MAPE)　of　120.2407,　significantly　outperforming　other　comparative　models　in　prediction　performance,　providing　a　valuable　reference　for　improving　magnetic　component　loss　prediction　methods.　©　2025　IEEE.