High concentration of nitrates in water will not only cause water environment pollution but also pose a great threat to human health. The traditional methods for detecting nitrates have a long detection time and are complex to operate. In view of the difficulty in rapid on-line detection of nitrate nitrogen in water, a method combined a mixed prediction model with spectral integration was proposed to rapidly detect nitrate concentration in water based on ultraviolet absorption spectroscopy. The mixed prediction model is a model after data fusion of the dual wavelength prediction model established by low-concentration samples and the partial least-squares support vector machine (LS-SVM) prediction model based on the high concentration samples. According to the appropriate concentration gradient, 19 sets of nitrate nitrogen standard solution were equipped, and the spectral data of nitrate nitrogen samples of different concentrations were measured by experiment. First, Regression analysis was performed on all samples based on the dual wavelength method. Absorbance A was calculated for different experimental samples according to A=A220-2A275, where A220 and A275 were the absorbance of the samples at 220 and 275 nm. The values were linearly regressed to fit the predicted values of the sample concentrations. The results showed that when the sample concentration is small, the correlation is very good, and r is 0.997 4. The two-wavelength method is only suitable for the establishment of low-concentration samples prediction model with a serious nonlinear drift in the rising curve of the experimental samples concentration. For high-concentration samples, spectral overlap is severe and it is suitable for establishing nonlinear prediction models. Both support vector machine (SVM) and partial LS-SVM are suitable for nonlinear data modeling of small samples. The LS-SVM has a slightly higher prediction accuracy and a slightly faster operating speed. By performing full-wavelength spectral integration on all experimental samples and comparing the rate of change of the spectral integrals of adjacent samples, the critical concentration of the sample can be selected. The 4 mg·L-1 nitrate sample has the largest change rate before and after the integrated value, so it is appropriate to select 4 mg·L-1 as the the critical concentration value. The LS-SVM prediction model was established for experimental samples with concentrations higher than 4 mg·L-1. Cross-validation methods were used to select the appropriate parameters. The regularization parameter was γ=50, and the Gaussian kernel function width was σ2=0.36. The other samples were used to establish the dual-wavelength prediction model, and finally performed the data fusion of the two models, which formed the detection of nitrate from low concentration to high concentration. In order to verify the prediction accuracy of the mixed prediction model, the model of SVM, LS-SVM and PLS was established, and evaluated the model with mean absolute error (MAE), correlation coefficient (r), and root mean squared error (RMSE). The verification results showed that compared with other models, the correlation coefficient of the proposed mixed model regression is 0.999 86, which is increased by 1.8%, 1.6%, and 0.45% respectively, and the average absolute error between the predicted value and the true concentration is 2.55%, which decreased by 6.27%, 4.49%, and 1.01% respectively, and the root-mean-square error is 0.303, which is the smallest of the four prediction models. The relative error of SVM and LS-SVM is relatively high, and PLS model fluctuates up and down relatively. The relative error of mixed forecasting model is the most stable and remains at a low level, and the forecasting effect of mixed forecasting model is obviously better than that of other models. Compared with the measurement method in ［5-7］, this hybrid prediction method can simply and quickly measure the nitrate nitrogen concentration in water without reagents and no secondary pollution, andthe prediction accuracy is significantly improved compared with the model in [9]. Therefore, the proposed mixed model can correctly and quickly predict the concentration of nitrate in water, and provide an effective technical reference for on-line monitoring of nitrate concentration in water.