The presence of suspended solids would seriously affect the accuracy of the output results of the spectral absorption method, for monitoring the chemical oxygen demand (COD) concentration of water quality. To address this issue, this research utilized a chemometric method, coupled with a high-sensitivity UV-Vis-IR spectrophotometer, to conduct spectral analysis in a dark room environment at room temperature. The spectral absorption characteristics of COD and kaolinize solutions with different concentration ratios were systematically explored, through cross experiments. The turbidity compensation algorithm was studied in detail by constructing a function of independent and dependent variables to develop a comprehensive model representation. A regression model was then constructed with logarithmic function fitting, and partial least squares regression algorithms. The research result demonstrates that the methodology yielded precise final output results, even in solutions with high concentration turbidity. This research represents a departure from the conventional turbidity compensation methodology, which provides a real-time correction and a flexible modeling method for the analysis of adjustment data for COD measurement based on spectral absorbance.