Nitrate nitrogen (NO3-N) is one of the “three nitrogen” (nitrate nitrogen, nitrite nitrogen, ammonia nitrogen) in water, can reflect the degree of pollution of the water environment, and is an important indicator of water quality assessment. High concentrations of nitrate nitrogen in the water body will not only lead to increased pollution of the water environment, but also pose a greater threat to humans, animals and aquatic products. The traditional nitrate nitrogen detection must be measured after the reaction, with a long time, complex operation, secondary pollution and other disadvantages. The spectrometry method has the significant advantages of rapid, non-destructive, and no reagent consumption. To address the problem that nitrate nitrogen is difficult to be detected quickly, a method for rapid quantitative analysis of nitrate nitrogen based on UV absorption spectroscopy was proposed. The UV absorption spectra of 42 samples of nitrate nitrogen standard solutions with concentrations ranging from 0 to 20 mg·L-1 were collected, and each sample was averaged 11 times to reduce the influence of instrument noise and environment. The SPXY algorithm was used to divide the training set and test set according to the ratio of 7∶3, and the UV absorption spectra data were preprocessed using the Savitzky-Golay (SG) filtering algorithm. The appropriate regularization parameter λ=0.203 6 was obtained by 10-fold cross-validation with Lasso regression, and then the Lasso regression was used to filter out the correlations with nitrate nitrogen in the full spectral range. The spectral features associated with nitrate nitrogen were selected in the full spectral range using Lasso regression. The absorbance at the feature wavelength was fitted with the sample concentration by partial least squares (PLS) to establish the regression model for nitrate nitrogen. The R2 and RMSE of the training set of the model built using the modeling method proposed in this paper were 0.999 91 and 0.060 15, respectively, and the R2 and RMSE of the test set were 0.999 72 and 0.046 91, respectively. In order to verify the effect of the SG-Lasso-PLS prediction model proposed in this paper, additional Lasso-PLS, SG-PCA-PLS and SG-PCA-PLS were built. PLS and SG-PCA-SVR prediction models were compared. The validation results show that the R2 and RMSE of the prediction models established by SG-Lasso-PLS are better than those of the other three. It indicates that SG filtering can eliminate the spectral signal’s random noise and improve the model’s prediction accuracy. Compared with the PCA data dimensionality reduction algorithm, Lasso can achieve spectral feature selection and data dimensionality reduction in the full spectral range, which can effectively eliminate the redundant information of spectral data and improve the model’s prediction accuracy. Therefore, the hybrid SG-Lasso-PLS model proposed in this paper can quickly and accurately predict the nitrate nitrogen in water bodies. As a basic study of nitrate nitrogen concentration detection, it can provide an algorithmic reference for fast and pollution-free water quality online monitoring scenarios.