Chemical Oxygen Demand (COD) is an important water quality parameter, which is generally used to reflect the degree of organic pollution. The detection of COD has long relied on laboratory chemical analysis methods after sampling. The most commonly used method is potassium dichromate oxidation and acid Potassium Permanganate oxidation. However, the chemical analysis methodsarecomplicated, time-consuming and labor-intensive. Moreover, the introduction of new chemicals by these methods causes secondary pollution. Therefore, there is an urgent need for a detection technique that enables rapid measurement of COD in water. Based on the previous research, the test method of COD by the laser-induced breakdown spectroscopy was explored in this paper. The focus was to optimize the model prediction speed in order to study therapid measurement of COD in waterbythe techniqueof laser-induced breakdown spectroscopy. We collected 99 water samples with different COD concentrations, and divided them into two groups: training set and testing set. The COD concentration of each water sample was measured by potassium dichromate oxidation method. And the spectral information of each water sample at the wavelengths of 200～1 000 nm was collected by the laser-induced breakdown spectroscopy acquisition system built by our laboratory. Partial least squares (PLS) algorithm was used to establish a quantitative measurement model for COD of training set samples and the spectral data of test set were predicted. The predicted results were compared with the real values measured by laboratory chemical methods to evaluate the predicted results. By analyzing the prediction model established by the original spectrum, it was found that a large number of laser-induced breakdown spectral data have poor correlation with COD concentration during the modeling process, and these useless data participated in the calculation, wasting computing resources, dragging the detection time, causing the system load to be too large, which was not conducive to the development of portable detection equipment. We focused on the first few principal components with the largest contribution. By analyzing the principle of COD measurement and the load of PLS model, we found the main characteristic peaks of LIBS spectrum which have the highest correlation with the concentration of COD in water. These characteristic peaks belong to C, H, O, N and some reductive ion elements in water. Most of C, H, O and N come from organic matter in water whose characteristic peaks have the greatest contribution to the prediction ability of COD model. The definition of COD reflects the amount of these elements in the water body, which is consistent with our analysis conclusion. In order to improve the detection speed, we extracted these characteristic peaks, and eliminated a large number of unrelated or low-correlation data. After many times of screening and dimensionality reduction, the original 13 622 data of each spectrum were reduced to 28, which greatly reduced the computational complexity of the system, but still retains good prediction ability. The 28 characteristic wavelengths selected are the best ones to reflect the concentration of COD in water, which lays a foundation for the development of portable multi-band detection equipment for COD in water and the rapid measurement of COD.