Abstract: Within the framework of digital-intelligent education, this study develops a digital teaching assistance system for analytical chemistry experiments by integrating machine learning and big data technologies, using water hardness determination as a representative example. The system captures titration images and employs color histograms combined with a support vector machine (SVM) model to determine titration endpoints, enabling automated result analysis, evaluation, and personalized learning feedback. This research successfully bridges traditional experimental methods with digital technologies, fostering students’ competencies in data analysis, interdisciplinary thinking, and practical skills. The proposed approach offers an innovative methodology for promoting digital transformation in university-level analytical chemistry laboratory education.

Key words: Digital teaching, University-level chemistry experiments, Machine learning, Digital and intelligent