Existing　multi-person　pose　estimation　methods　show　a　tendency　for　the　parameters　in　the　model　to　become　increasingly　large　in　order　to　improve　generalisation　performance,　requiring　huge　computational　resources.　Therefore,　we　propose　a　lightweight　multi-person　pose　estimation　method　called　PKDHP　(Process　Knowledge　Distillation　for　Human　Pose　estimation),　which　applies　knowledge　distillation　to　pose　estimation.　PKDHP　treats　the　training　process　of　student　models　in　knowledge　distillation　as　a　human　learning　process,　specifically,　PKDHP　proposes　the　CABF　module　to　replace　the　ABF　module　in　ReviewKD　aiming　to　guide　the　lower　level　feature　through　the　model＇s　higher　level　feature,　and　proposes　the　Transfer　knowledge　distillation　method　to　force　the　student　model　to　imitate　the　process　of　feature　transfer　from　the　teacher　model,　thus　further　simulating　the　human　learning　style.　Compared　with　other　knowledge　distillation　methods,　we　use　the　same　model　parameters　on　two　multi-person　pose　estimation　datasets　(COCO　and　MPII)　to　achieve　higher　performance.　©　2023　SPIE.