With　the　application　of　artificial　intelligence　technology　in　sports　activity　monitoring,　new　technologies　such　as　interactive　robots　and　the　Internet　of　Things　are　being　more　widely　used,　which　can　provide　entertainment　and　interactive　guidance　for　sports　training.　This　article　analyzes　the　research　on　human　pose　recognition　and　sports　activity　monitoring　of　entertainment　robots　based　on　artificial　intelligence　interaction.　This　paper　designs　and　develops　an　entertainment　robot　system　that　can　recognize　human　posture　and　monitor　physical　activities.　Users　can　interact　with　the　robot　and　get　real-time　feedback　about　their　posture　and　activities,　thereby　improving　motor　skills　and　physical　fitness.　After　in-depth　analysis　of　the　theory　of　dynamic　Bayesian　network,　we　used　EM　algorithm　to　build　a　dynamic　Bayesian　network　model,　carried　out　detailed　research　on　the　random　variables　represented　by　the　nodes　of　the　dynamic　model,　and　used　the　Internet　of　Things　human　posture　recognition　technology　to　extract　the　features　of　human　behavior　according　to　the　characteristics　of　human　behavior.　The　results　show　that　the　attitude　recognition　accuracy　can　be　improved　by　adding　the　dynamic　Bayesian　network　based　on　the　random　forest　algorithm.　The　entertainment　robot　can　accurately　identify　human　posture　and　monitor　sports　activities,　and　has　good　real-time　and　stability.　Through　interaction　with　the　robot,　users　can　get　personalized　suggestions　for　individual　postures　and　activities,　improving　the　training　effect　and　enjoyment　of　physical　activities.　Therefore,　through　the　in-depth　research　and　analysis　of　the　dynamic　Bayesian　network　and　the　human　posture　recognition　of　the　Internet　of　Things,　it　is　found　that　the　sports　activity　monitoring　system　proposed　in　this　paper　can　comprehensively　monitor　students＇　sports　activities,　so　as　to　achieve　the　goal　of　comprehensively　improving　students＇　physical　fitness.