With the rapid development of China’s economy, surface water pollution has become increasingly serious. Therefore, it is of vital importance to realize the continuous monitoring of surface water quality to ensure human health and protect the environment. Nitrite nitrogen concentration is an important index in water quality assessment. Polluted water poses a great threat to human, livestock and aquatic products. The detection of organic pollutants by UV-Visible absorption spectrum has become an important method for water quality detection. A few papers about the detection of nitrite nitrogen concentration in water by UV-Vis spectroscopy in China can be found. Most methods require chemical pretreatment of water samples and then use UV spectrophotometer to predict the nitrite nitrogen concentration. These methods are tedious, time-consuming and labor-consuming so thatthey can’t realize real-time continuous detection. Besides, they will cause further environmentpollution. The detection of nitrite nitrogen concentration without chemical pretreatment based on UV-Vis absorption spectrometry is rarely mentioned in the literature. Therefore, UV-Vis spectroscopy was adopted in this paper to carry out basic research on unattended automatic continuous monitoring of surface water quality. Nitrite nitrogen solution samples were preparedand a three-day experiment was designed to measure the ultraviolet and visible spectra of all samples (group D1, group D2, and group D3) respectively every day. Firstly, samples from group D1 and group D2 were modeled respectively by partial least squares regression method (PLSR). The mean absolute percentage error (MAPE) obtained by full cross validation was 1.19% and 1.85% respectively. The result shows that the PLSR model has good prediction accuracy. Secondly, in order to verify the adaptability of PLSR model under different measurement conditions, the experimental data of groupD1 and group D2 were used for mutual prediction analysis. The MAPE was respectively 3.36% and 4.51%, less than 5%, indicating that PLSR model has good robustness. Finally, all samples from groupD1 and group D2 were used for PLSR modeling, and samples from group D3 were used as the test set. The MAPE of the test set was 2.19%. The results show that the MAPE of the PLSR algorithm based on UV-Vis spectral analysis technique for detecting the nitrite nitrogen concentration in solution is controlled under 5%, better than similar reports. In addition, the modeling process of PLSR model is simple and the operation time is short. The model is simple in structure and easier to be transplanted and solidified into embedded systems, bringing convenience to the later development and design of portable devices. As the basic research of the detection of nitrite nitrogencon centration in surface water, this paper can provide guidance for the accurate and rapid detection of surface water quality in the future.