Abstract: The preparation of cis(trans)-diglycine copper complexes represents a classic experiment in geometric isomer synthesis. To address the challenges of comprehending microscopic mechanisms, controlling reaction endpoints, and improving yields—despite the easily observable experimental phenomena—we have innovatively integrated quantum chemical calculations and color recognition technologies. Using Gaussian 09 with its visualization tool GaussView, we performed density functional theory (DFT) calculations to analyze geometric structures, energies, dipole moments, and electrostatic potentials of both cis- and trans-diglycine copper(II) complexes. This approach enables students to apply quantum chemical methods for investigating geometric isomer properties while gaining fundamental insights into their structural differences at the molecular level. Concurrently, we developed a machine vision-based color recognition system using C++ programming and OpenCV library. This system employs camera-captured reaction images analyzed through HSV color space modeling to monitor real-time solution color changes during the cis-to-trans isomerization process, thereby achieving precise endpoint determination. The synergistic incorporation of quantum chemical computation and color recognition technology into experimental teaching not only enhances students’ understanding of structure-property relationships but also improves experimental accuracy and reliability. Furthermore, this integrated approach effectively cultivates students’ scientific reasoning, interdisciplinary application skills, and digital intelligence competencies.

Key words: Diglycine copper complex, Geometric isomerism, Density functional theory, Color recognition, Digital intelligence