As the most important pigment involved in photosynthesis of plant, the chlorophyll is an important indicator for monitoring bamboo pests. This study aims to establish the hyperspectral estimation model for the chlorophyll content of bamboo leaves under pests stress by wavelength screening of different spectral data sets, and provide a theoretical basis for monitoring the pests of bamboo by hyperspectral remote sensing. The test was carried out in Shunchang County, the bamboo production base in Fujian Province. The ASD FieldSpec 3 spectrometer was used to collect 102 bamboo leaves spectra of different pest levels, and the chlorophyll content of the corresponding leaves was determined by SPAD-502 chlorophyll meter. By comparing the spectral characteristics of bamboo leaves with different pest levels, the mechanism of estimating chlorophyll content with hyperspectral data was explored. The original spectrum (OS) of the bamboo leaves was subjected to continuum removal (CR), first derivative (FD), and continuum removal-first derivative (CR-FD), and the correlation between different spectral data and chlorophyll content was analyzed. The characteristic wavelengths of the four spectra were extracted by the successive projection algorithm (SPA). Four spectral datasets were divided by sample set partitioning based on joint x-y distances method (SPXY) and random method. Combined with multiple stepwise regression (MSR), the chlorophyll content estimation model of bamboo leaves was established, and the effects of spectral transformation and sample partitioning on estimating chlorophyll content were analyzed. The results showed that there were significant differences in the spectral reflectance of bamboo leaves with different pest levels. The main manifestations were the gradual disappearance of the “green peak” and “red valley” in the visible light range, the “red edge” was levelled and the near-infrared wavelength reflectance was reduced. The spectral transformation could effectively improve the correlation between the spectrum and chlorophyll content, and the correlation coefficient between the CR-FD spectrum and chlorophyll content at 724 nm was the largest. The characteristic wavelengths of different spectral data sets extracted by the successive projection algorithm were concentrated in the green band, red band, and “red edge”, and the multiple selected wavelengths were located in bands (600～750 nm) that highly correlated with chlorophyll content. The MSR model based on SPXY sample partitioning method could significantly improve the estimation accuracy of chlorophyll content compared with the random sample partitioning method, in which R2 and RPD increased by 0.1 and 0.5, and RMSE decreased by 0.7 on average. The multiple stepwise regression model established by CR-FD spectrum characteristic wavelengths combined with SPXY sample partitioning method had the highest accuracy for estimating chlorophyll content of bamboo leaves, and the R2, RMSE, RPD were 0.835, 2.604 and 2.364 respectively, which could accurately estimate the chlorophyll content of bamboo leaves under pests stress.