Abstract: Aiming at the problem that the method of measuring flavonoids in wild Acanthopanax senticosus leaves is cumbersome, the time is longer and the leaf needs to be destroyed. In this paper, a hyperspectral model for estimating the content of flavonoids in Acanthopanax senticosus leaves was proposed. The spectral characteristics of the leaves were analyzed. The true content of flavonoids in the leaves was measured by drying, grinding and ultraviolet spectrophotometer. The results showed that the true content of flavonoids in the leaves was mixed by smoothing（SG）, multiple scattering correction（MSC/EMSC）, standard normal transformation（SNV） and first derivative（1 Der） the original spectrum was preprocessed in a combined manner. after comparison, snv and 1 der were selected to combine each other as the final preprocessing method. through the combination of spa and pca algorithm, the characteristic band was selected and the principal component of pc1 and pc2 total sum of 97% was selected. after verifying the correlation of the reflectivity of the characteristic band with the 40 groups of prediction sets by matlab2018 a, then select the predicted values and 20 groups of measured values to model with bp neural network and support vector machine, respectively. The experimental results show that the correction set coefficient R of the model based on BP neural network R_c~2 at 0.8649, 0.7976,0.8485, respectively. the model established by support vector machine has a correction set determination coefficient of R_c~2 of 0.7526,0.7742,0.7243, respectively. the modeling results show that the model constructed by combining snv and 1 der with bp neural network is the best. At the same time, it also provides a more powerful support for the inversion of flavonoids in the leaves of Acanthopanax senticosus, which will also improve the efficiency of industrial and medicinal picking, and further improve the utilization value of Acanthopanax senticosus.