The estimation model of soil alkali-hydrolyzable nitrogen content based on indoor hyperspectral data wasconstructed，which provided a new method for rapid and accurate acquisition of nutrient information in soil. 106 soil samples collected fromUrumqi，Xinjiang were air-dried，ground andsifted，and the reflectance spectral data were collected indoors. The collected spectral data were pretreated by Savitzky-Golayfiltering，first -orderdifferentiation(FDR)， continuumremoval(CR)and multiple scatteringcorrection(MSC). On thisbasis，continuous projection algorithm was used to screen the characteristic bands of the pre-treateddata，and partial least squares regression was used to establish a hyperspectral analysis model for predicting soil alkali-hydrolyzed nitrogen content after different pretreatment. Coefficient ofdetermination(R2)，root meansquareerror(RMSE)，relative analysiserror(RPD)and mean relativeerror(MAE) were used as evaluation indexes of the model. The results showed that the prediction accuracy of the continuous removal treatment was the most prominent among the four pretreatment methods. R2，RMSE，RPD and MAE were0.90，13.0，2.26 and0.13，respectively. The linear regression equation of the model wasy=0.9316x+8.763.Therefore，the continuous projection algorithm combined with partial least squares regression could be used to estimate the content of alkali-hydrolyzed nitrogen in soil in Urumqi. The results could provide a theoretical basis for rapid inversion of soil alkali-hydrolyzed nitrogen content with indoor hyperspectral data.