Abstract: Determination of the sucrose conversion rate constant by polarimetry is a classic experiment of physical chemistry. However, temperature fluctuations and manual reading errors during the experiment often lead to deviations in the results. In this study, the traditional polarimetric method was improved through the addition of a temperature-controlled setup and the incorporation of AI-based data analysis, providing a dual optimization of both the experimental apparatus and data processing. AI algorithms were employed for automatic data fitting, error detection, and parameter optimization, effectively enhancing the accuracy and reproducibility of the rate constant determination. The results demonstrate that the improved experimental scheme offers higher stability and reliability, while effectively promoting students’ data analysis skills and experimental innovation and significantly enhances the scientific rigor and demonstrative value of experimental teaching.