Purpose: The purpose of this paper is to propose an indexing and teaching focus mining system for lecture videos recorded in an unconstrained environment. Design/methodology/approach: By applying the proposed algorithms in this paper, the slide structure can be reconstructed by extracting slide images from the video. Instead of applying traditional shot-change detection methods for general videos, a new edge-based shot-change detection algorithm is designed specifically for lecture videos. Besides, light influence and occlusions in the lecture video can be removed to obtain more accurate results. Moreover, the teaching focus can be extracted according to instructors' behavior based on the analyses of visual and audio information extracted from the lecture video. Findings: Experiment results show the feasibility of the proposed method, that is, the slide shots can be correctly detected even if the illumination conditions are variant or the slides are obstructed by the instructor or students, and the teaching focus can be extracted to provide learners with an efficient way to study. Research limitations/implications: This paper provides only technical experiments, but lacks complete educational study. In the future, more subjective tests will be designed to examine the educational effects on students. Practical implications: This paper proposes a practical indexing and teaching focus mining system for lecture videos which can help students learn. Originality/value: The proposed algorithms for indexing and teaching focus mining are derived and applied in lecture videos in this paper. (Contains 22 figures and 7 tables.)