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 (ρ733.66～898.56, ρ′562.95～585.25, ρ′706.18～725.41) 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 R2 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 R2 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.