The　objective　of　this　study　was　to　deeply　understand　the　adaptation　mechanism　of　the　functional　traits　of　Moso　bamboo　Phyllostachys　pubescens　syn.　edulis　(Poales:　Poaceae)　leaves　to　the　environment　under　different　Pantana　phyllostachysae　Chao　damage　levels,　analyzing　the　changes　in　the　relationship　between　specific　leaf　area　(SLA)　and　leaf　dry　matter　content　(LDMC).　We　combined　different　machine　learning　models　(decision　tree,　RF,　XGBoost,　and　CatBoost　regression　models),　and　used　different　canopy　heights　and　different　levels　of　infestation,　to　analyze　the　changes　in　the　relationship　between　the　two　under　different　levels　of　infestation　based　on　the　results　of　the　best　estimation　model.　The　results　showed　the　following:　(1)　The　SLA　of　Ph.　pubescens　showed　a　decreasing　trend　with　the　increase　om　insect　pest　degree,　and　LDMC　showed　an　inverse　trend.　(2)　The　SLA　of　bamboo　leaves　was　negatively　correlated　with　the　LDMC　under　different　insect　pest　degrees;　the　correlation　of　the　data　under　the　healthy　class　was　higher　than　that　of　other　insect　pest　levels,　and　at　the　same　time　better　than　that　of　the　full　sample,　which　laterally　confirmed　the　effect　of　insect　pest　stress　on　the　functional　traits　of　Ph.　pubescens　leaves.　(3)　When　modeling　under　different　infestation　levels,　the　CatBoost　model　was　used　for　heavy　damage　and　the　RF　model　was　used　for　the　rest　of　the　cases;　the　decision　tree　regression　model　was　used　when　modeling　different　canopy　heights.　The　findings　contribute　certain　insights　into　the　nuanced　responses　and　adaptive　mechanisms　of　Ph.　pubescens　forests　to　environmental　fluctuations.　Moreover,　these　results　furnish　a　robust　scientific　foundation,　essential　for　ensuring　the　enduring　sustainability　of　Ph.　pubescens　forest　ecosystems.