Pantana　phyllostachysae　Chao　is　a　destructive　leaf-eating　pest　that　poses　a　significant　threat　to　the　health　of　bamboo　forests　and　the　bamboo　industry.　However,　the　spatial　and　temporal　spread　mechanisms　of　this　pest　are　still　unclear.　To　better　understand　and　predict　the　spread　of　this　pest,　we　used　Sentinel-2　A/B　images　from　the　pest　detection　period　of　2018-2021,　to　identify　association　factors　from　five　dimensions,　including　forest　stand,　meteorology,　topography,　pest　sources,　and　human　environment　factors.　The　association　factor　sets　for　the　spread　of　P.　phyllostachysae　were　established　under　both　existence　and　nonexistence　pest　control　scenarios.　The　extreme　gradient　boosting　(XGBoost)　model　was　employed　to　derive　conversion　rules　for　the　respective　spread　models,　enabling　the　determination　of　suitability　probabilities　for　both　healthy　and　damaged　bamboo　forests.　These　probabilities　were　then　utilized　in　conjunction　with　cellular　automata　(CA)　to　simulate　the　spread　of　P.　phyllostachysae　under　two　scenarios.　The　results　showed　that　the　OA　and　Kappa　reached　more　than　85%　and　0.7%　in　both　scenarios,　respectively.　Meanwhile,　the　division　of　pest　control　scenarios　and　the　selection　of　XGBoost　both　help　to　improve　the　spreading　simulation　accuracy.　Our　models　effectively　coupled　the　research　results　of　leaf　hosts　of　different　damage　levels,　simulated　the　spread　of　P.　phyllostachysae,　and　identified　the　dynamic　mechanisms　of　the　pest＇s　spread.　These　findings　provide　decision　support　for　interrupting　the　spread　path　of　the　pest　and　achieving　precise　control,　thus　safeguarding　forest　ecological　security.