The elective course "Laboratory Mouse Histopathology and Digital Atlas" is designed as an interdisciplinary and cutting-edge offering for undergraduate students in biomedical-related majors, aiming to equip students with the skills for digital cognition and intelligent analysis of laboratory mouse histopathology. In response to the increasing application of digital pathology and artificial intelligence (AI) in biomedical research, this course has developed a systematic digital pathology atlas library of mice and a lightweight AI analysis toolchain. It features a progressive pedagogical framework comprising "theoretical foundation-digital observation-AI quantification-comprehensive inquiry", and employs a competency-based, multidimensional evaluation system. The course emphasizes the deep integration of morphological knowledge with computational thinking, thereby providing a systematic teaching paradigm for cultivating interdisciplinary talents with digital literacy in the context of "New Medical Science". This paper systematically presents the overall framework, content design, and implementation pathway of the course, aiming to offer a reference for the digital and intelligent transformation of medical experimental teaching.