Pest　detection　algorithm　research　is　an　important　guarantee　to　precisely　and　rapidly　monitor　the　forest　pest　and　forest　protection　and　quarantine.　Based　on　the　external　morphology　of　the　host　and　its　internal　physiological　phenomena,　taking　the　leaf　loss　(LL),　relative　chlorophyll　content　(RCC),　relative　water　content　(RWC),　and　the　three　spectral　values　of　the　characteristic　wavelengths　(rho　733.66　similar　to.898.56,　rho＇562.95　similar　to　585.25,　rho＇706.18　similar　to　725.41)　as　the　experimental　data　which　were　randomly　divided　into　experimental　group　(63)　and　verificantion　group　(37)　with　5　repeated　tests,　then　the　models　of　Fisher　discriminant　analysis,　random　forest　and　BP　neural　networks　for　pest　levels　were　constructed.　The　detection　accuracy,　Kappa　coefficient　and　R-2　were　used　to　comprehensively　compare　the　detection　effects　of　these　three　algorithms.　The　results　showed　that　the　detection　accuracy　of　Fisher　discriminant　analysis,　BP　neural　networks　and　random　forest　were　69.19%,　65.41%　and　83.78%,　and　Kappa　coefficient　were　0.576　9,　0.532　4　and　0.778　8,　and　R-2　were　0.722　2,　0.582　6　and　0.870　9.　Overall,　all　of　these　algorithms　have　the　capability　of　pest　detection,　among　which,　the　detection　effect　of　the　random　forest　is　the　best,　and　Fisher　discriminant　analysis　is　secondly,　and　BP　neural　networks　is　thirdly.　Besides,　the　accuracy　of　random　forest　detection　is　superior　to　that　of　Fisher　discriminant　analysis　and　BP　neural　networks　in　non-damage,　mild　damage　and　severe　damage,　but　these　three　methods　have　insufficient　detection　accuracy　for　moderate　damage　level.　The　results　could　be　a　reference　tothe　selection　of　detection　algorithm　in　P.　chao　and　other　types　of　diseases　and　insect　pests,　building　a　strong　foundation　for　further　study.