The presence of suspended solids can significantly impact the accuracy of measuring the chemical oxygen demand (COD) concentration in water quality using the spectral absorption method. To address this issue, this research utilized a chemometric method in conjunction with a high-sensitivity ultraviolet-visible-infrared (UV-VIS-IR) spectrophotometer to conduct spectral analysis in a dark room environment at room temperature. The spectral absorption characteristics of COD and kaolin solutions with varying 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 constructed using logarithmic function fitting and partial least squares regression (PLSR) algorithms. The research results demonstrate that the methodology yields precise final output results, even in solution with high concentration turbidity. This research presents 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.