The　aim　of　Knowledge　Distillation　(KD)　is　to　train　lightweight　student　models　through　extra　supervision　from　large　teacher　models.　Most　previous　KD　methods　transfer　feature　information　from　teacher　models　to　student　models　via　connections　of　feature　maps　at　the　same　layers.　This　paper　proposes　a　novel　multi-level　knowledge　distillation　method,　referred　to　as　Normalized　Feature　Fusion　Knowledge　distillation(NFFKD).　The　proposed　model　learns　different　levels　of　knowledge　to　improve　the　network　performance.　We　proposed　to　use　the　hierarchical　mixed　loss(HML)　module　to　minimize　the　gap　between　the　intermediate　feature　layers　of　the　teacher　and　the　student,　and　the　teacher-student　gap　is　reduced　by　normalizing　the　logits.　Experimental　results　have　demonstrated　that　the　proposed　NFFKD　shows　superiority　over　several　state-of-The-Art　KD　methods　on　public　datasets　under　different　settings.　©　2022　IEEE.