Abstract: The sucrose hydrolysis reaction is a classic kinetic experiment in physical chemistry education. Building on a previously developed experimental system that integrates intelligent recognition of the polarimeter’s field of view and automatic optical rotation measurement, this study introduces a series of software optimizations. Built-in functional modules for training set recording, model management, and fitting analysis were developed to enhance operational efficiency. The stepper motor control strategy was refined to allow flexible adjustment of the stepping mode, thereby improving the response speed and flexibility of dynamic measurements. The image preprocessing stage was optimized to enhance the quality of both training and measurement data, which in turn strengthened the generalization capability of the optical rotation measurement. Additionally, the user interface was redesigned using a tab-based layout, improving the modularity of functional components and the clarity of the operational logic. Experimental validation demonstrated that these optimizations effectively improved experimental efficiency and data quality, reduced the operational burden on students, and provided a more practical tool to support intelligent and digital teaching in the context of sucrose hydrolysis experiments.

Key words: Sucrose hydrolysis, Polarimeter, Machine learning, Automated measurement, Software optimization