In the field of soil digital mapping, precision agriculture and soil resource investigation, the study of aerial hyperspectral data to provide scientific prediction results by aerial hyperspectral have become the focus of research, especially in the case of black soil rich in nutrients in Northeast China. Compared with the main components of soil in the black soil, selenium is a trace element, whose effect on the normal growth of crops is as important as a large number of elements, and it is also a necessary nutrient element for human. In this paper, an indirect extraction model based on the main component is created for the retrieval of selenium content. This model can significantly increase the regression coefficient of selenium content and reduce the error between the measured value and the predicted value. The data source is CASI-1500 aerial hyperspectral imaging system with a spectral range of 380~1 050 nm, and a spatial resolution of 1.5 m. 60 soil samples were collected from the Jiansanjiang area of Heilongjiang. The data of selenium, organic matter, total iron, pH and calcium oxide content were obtained. The BP neural network was selected to establish the inversion model of spectrum and content. In addition, the law of spectral change in the visible and near infrared range of different content of black soil composition was analyzed, and the rule that the spectral reflectance would increase gradually as the content of selenium increased. However, when the selenium content was low, the law would gradually weaken until the other components are disturbed. The spectral characteristics of organic matter were opposite to that of selenium. In general, the reflectance decreases as the content increases, which is closely related to the spectral properties of organic matter. The spectra of the total iron showed similar laws with the organic matter spectrum, indicating that the two have high correlation. The spectral characteristics and detection values of different pH values and calcium oxide contents did not show obvious characteristics, and the law of reflection was not obvious. The correlation coefficients of nutrient contents in different nutrient contents of 60 sampling points were obtained by bands. The results show that the correlation coefficient of each band of pH is the highest, the mean value is 0.63, the second is the correlation coefficient of total iron, 0.54, the correlation coefficient of organic matter and calcium oxide is close to 0.42 and 0.47, while the average correlation coefficient of selenium element content and bands is the lowest, which is 0.38. The first 5 bands with higher correlation coefficients are selected as modeling bands. The characteristics of selenium are 447, 437, 456, 466 and 475 nm; the characteristic bands of organic matter are 447, 456, 466, 437 and 475 nm; the characteristic bands of the whole iron are 752, 695, 800, 762 and 733 nm, and the characteristics of pH are 905, 752 and 695 nm. By calculating the correlation coefficient of sample point selenium content and other components, selenium has a positive correlation with organic matter, and the correlation coefficient is 0.79. The correlation coefficient is negatively correlated with total iron, pH and calcium oxide, and the correlation coefficients are -0.80, -0.94 and -0.69, respectively. In view of the high precision of the inversion of organic matter, total iron, pH and calcium oxide, while the content of selenium is low and the accuracy of direct inversion is insufficient, a method of extracting the functional relationship of selenium elements by extracting the content of four components is designed, and the content of selenium elements is indirectly retrieved. First, the five components and characteristic spectra are analyzed by using neural network, and the regression coefficients R2 and RMSE of each component are calculated. It is concluded that total iron and pH have higher inversion accuracy, while organic matter and calcium oxide coefficient are lower than 0.8, but they are also significantly higher than those of selenium. A regression model for the content of selenium and other four components was obtained, and Se=0.522 9+0.041 8 Som-0.016 6 Fe2O3-0.035 6 pH-0.005 CaO. The selenium element was extracted indirectly, the regression coefficient increased from 0.516 to 0.724, the root mean square error was reduced from 0.182 to 0.136 based on this model, which improved the accuracy of the selenium content inversion, and provided a new technique for the precise mapping of selenium elements in a large scale.