The　construction　of　a　pest　detection　algorithm　is　an　important　step　to　couple　＂ground-space＂　characteristics,　which　is　also　the　basis　for　rapid　and　accurate　monitoring　and　detection　of　pest　damage.　In　four　experimental　areas　in　Sanming　City,　Jiangle　County,　Sha　County　and　Yanping　District　in　Fujian　Province,　sample　data　on　pest　damage　in　182　sets　of　Dendrolimus　punctatus　were　collected.　The　data　were　randomly　divided　into　a　training　set　and　testing　set,　and　five　duplicate　tests　and　one　eliminating-indicator　test　were　done.　Based　on　the　characterization　analysis　of　the　host　for　D.　punctatus　damage,　seven　characteristic　indicators　of　ground　and　remote　sensing　including　leaf　area　index,　standard　error　of　leaf　area　index　(SEL)　of　pine　forest,　normalized　difference　vegetation　index　(NDVI),　wetness　from　tasseled　cap　transformation　(WET),　green　band　(B-2),　red　band　(B-3),　near-infrared　band　(B-4)　of　remote　sensing　image　are　obtained　to　construct　BP　neural　networks　and　random　forest　models　of　pest　levels.　The　detection　results　of　these　two　algorithms　were　comprehensively　compared　from　the　aspects　of　detection　precision,　kappa　coefficient,　receiver　operating　characteristic　curve,　and　a　paired　t　test.　The　results　showed　that　the　seven　indicators　all　were　responsive　to　pest　damage,　and　NDVI　was　relatively　weak;　the　average　pest　damage　detection　precision　of　six　tests　by　BP　neural　networks　was　77.29%,　the　kappa　coefficient　was　0.6869　and　after　the　RF　algorithm,　the　respective　values　were　79.30%　and　0.7151,　showing　that　the　latter　is　more　optimized,　but　there　was　no　significant　difference　(p　＞　0.05);　the　detection　precision,　kappa　coefficient　and　AUC　of　the　RF　algorithm　was　higher　than　the　BP　neural　networks　for　three　pest　levels　(no　damage,　moderate　damage　and　severe　damage).　The　detection　precision　and　AUC　of　BP　neural　networks　were　a　little　higher　for　mild　damage,　but　the　difference　was　not　significant　(p　＞　0.05)　except　for　the　kappa　coefficient　for　the　no　damage　level　(p　＜　0.05).　An　＂over-fitting＂　phenomenon　tends　to　occur　in　BP　neural　networks,　while　RF　method　is　more　robust,　providing　a　detection　effect　that　is　better　than　the　BP　neural　networks.　Thus,　the　application　of　the　random　forest　algorithm　for　pest　damage　and　multilevel　dispersed　variables　is　thus　feasible　and　suggests　that　attention　to　the　proportionality　of　sample　data　from　various　categories　is　needed　when　collecting　data.