Abstract: Taking the canopy leaves of ‘Red Leaf Green Peach’‘Big Venus Hawthorn’ and ‘Red Persimmon Tree’ as experimental materials, the hyperspectral data and corresponding SPAD data were obtained by field experiment.The traditional mathematical transform and wavelet transform were used to process and analyze the spectral data, and the partial least square algorithm was used to construct the estimation model of chlorophyll content, the method of accurately detecting the chlorophyll content of many kinds of fruit trees was studied, the law of the separation of spectral information by wavelet technology was analyzed, in order to provide reference for the fruit tree management.The results showed that, 1) wavelet technology could redistribute the absorption characteristics of different intensities, and the spectral absorption characteristics were gradually transferred from low-frequency information to high-frequency information, and the intensity of the absorption characteristics of high-frequency information content increases with the increase of decomposition scale.2) Compared with the original spectrum, mathematical transformation wavelet technology could significantly enhance the sensitivity of spectral data to chlorophyll content, and the maximum R~2 of the two and chlorophyll content could reach 0.879（located at 479 nm of H₈）.3) In the chlorophyll content estimation model based on spectral construction, the model constructed by wavelet technology H₆ had the highest accuracy and was the optimal model, and its validation sample R~2=0.941,RMSE=0.227,RPD=4.019,which indicates that the accurate detection of chlorophyll content in various fruit leaves by spectral technology was feasible and accurate.