Chemical oxygen demand (COD) is an important indicator of the degree of water pollution by organic matter. Ultraviolet absorption spectroscopy is the most widely used method for COD detection in water. It has the advantages of no pretreatment of samples, low cost, no pollution, and fast measurement speed. However, the original spectral data has high dimensions, and the spectral information contains a large number of redundant variables. Modeling the full spectral data has problems such as low accuracy and complicated calculations. Aiming at the low accuracy of UV absorption full-spectrum modeling and a large amount of collinearity in spectral data, this paper presents a method based on particle swarm optimization (PSO) and partial least squares (PLS) to select characteristic wavelengths to establish a prediction model. Improve the accuracy and applicability of the UV absorption spectrum prediction model and simplify the model. The UV spectrum data of 29 different concentrations of COD standard solutions were collected. Each standard solution was collected 5 times and averaged and smoothed to reduce the errors caused by the instrument and the environment. Taking into account the absorption of the standard solution in the spectral range of 200~310 nm, 246 wavelength points in this wavelength range were selected as modeling data, and the absorbance data at each wavelength point was used as a particle and numbered in order. PLS was used as the model Method, the correlation coefficient r and the root mean square error (RMSE) are used as evaluation indicators. The particle swarm algorithm fitness function f(x)=min (RMSE) is set. The initial population of particles is 20, the inertia weight w=0.6, and the self The learning factor c1=1.6, the group learning factor c2=1.6, the maximum number of iterations is 200, and the algorithm termination condition is to reach the maximum number of iterations. The output value of the optimal global variable of the algorithm is 168, 94, 181, 183, 175, 209, 106, 142. The correlation coefficient r and the predicted root mean square error RMSE of the PLS prediction model established by the eight wavelength points selected by the particle swarm optimization algorithm were 0.999 98 and 0.155 1, respectively. In order to verify the effectiveness of the prediction model established by PSO-PLS, three prediction models of PLS, iPLS and SVR were established for comparison. The verification results show that the correlation coefficient r and the root mean square error RMSE of the PSO-PLS model are better than those of the other three prediction models, which shows that the particle swarm algorithm can effectively extract the characteristic wavelengths used for PLS modeling and eliminate the common of sub-interval variables Linear, improving the accuracy of the prediction model. This method provides an effective way for real-time online monitoring of COD in water bodies.