Nitrate is an important index of water quality monitoring. The high concentration of nitrate in water results in the decrease of biodiversity and the degradation of the ecosystem. Meanwhile, it will cause irreversible harm to human health. Water quality monitoring technology based on the spectrum is the trend of modern water environment monitoring. Compared with the traditional method, nitrate field sampling and laboratory analysis, it has the advantages of simple operation, no pretreatment, fast detection, good repeatability and no pollution. Due to the complexity and diversity of water components, there is a high degree of nonlinearity between water parameters and absorbance. Traditional linear regression prediction models are not applicable, such as single wavelength method, dual wavelength method and partial least square method. Therefore, this paper proposes a new method for the determination of nitrate in water by fine full spectrum combined with the improved variable step grid search algorithm optimized support vector regression (GS-SVR). In cooperation with the college of chemistry and chemical engineering of Shaanxi University of Science and Technology, 94 groups of solution samples with different concentrations were prepared according to different concentration gradients and the experimental requirements by using standard nitrate solution, platinum cobalt standard solution and formazine standard suspension. Firstly, the transmittance spectrum was converted to absorbance, and 94 solution samples were divided into 80 training sets and 14 test sets by Kennard stone algorithm. Secondly, the improved GS algorithm combined with 5-fold cross validation is used to optimize the parameters of SVR by reducing the search range and changing the search step for many times, and the optimal penalty parameters and kernel function width are used to build model, which is used to predict the test set. Meanwhile, the prediction results are compared with those of BPNN, SVR, GS-SVR, PSO-SVR and GA-SVR. The results show that the coefficient of determination R²=0.993 5, root means square of prediction RMSEP=0.043 5. The optimal parameters are (512, 0.044 2), and the average training time is 13 s. Compared with the above five prediction models, R² increased by 1.22%, 11.66%, 0.78%, 0.74%, 0.77%, training efficiency increased by 4.15 times (BPNN), 8.30 times (GS-SVR), 21.38 times (PSO-SVR), 10.23 times (GA-SVR). The prediction accuracy and training efficiency of the model has been greatly improved, which provides a novel approach basis for rapid and real-time online monitoring of nitrate concentration in the complex water body. This method is also suitable for the establishment prediction models of other water quality parameters.