The　correlation　between　magnetic　Barkhausen　noise　(MBN)　features　and　the　surface　hardness　of　two　types　of　die　steels　(Cr12MoV　steel　and　S136　steel　in　Chinese　standards)　was　investigated　in　this　study.　Back-propagation　neural　network　(BP-NN)　models　were　established　with　MBN　magnetic　features　extracted　by　different　methods　as　the　input　nodes　to　realize　the　quantitative　prediction　of　surface　hardness.　The　accuracy　of　the　BP-NN　model　largely　depended　on　the　quality　of　the　input　features.　In　the　extraction　process　of　magnetic　features,　simplifying　parameter　settings　and　reducing　manual　intervention　could　significantly　improve　the　stability　of　magnetic　features.　In　this　study,　we　proposed　a　method　similar　to　the　magnetic　Barkhausen　noise　hysteresis　loop　(MBNHL)　and　extracted　features.　Compared　with　traditional　MBN　feature　extraction　methods,　this　method　simplifies　the　steps　of　parameter　setting　in　the　feature　extraction　process　and　improves　the　stability　of　the　features.　Finally,　a　BP-NN　model　of　surface　hardness　was　established　and　compared　with　the　traditional　MBN　feature　extraction　methods.　The　proposed　MBNHL　method　achieved　the　advantages　of　simple　parameter　setting,　less　manual　intervention,　and　stability　of　the　extracted　parameters　at　the　cost　of　small　accuracy　reduction.