Since blackboards are the standard in the classrooms and are still used today, blackboard lecture videos are common in the lecture video recordings. However, it has been known that content-based blackboard lecture video analysis is challenging and thereby rarely be touched upon in the field of semantic computing. In this paper, we proposed a new structuring method for blackboard lecture videos by estimating the learning focus that learners should pay more attention to. Both visual and aural analysis for blackboard lecture videos are utilized and integrated to develop a learning-focused attention model. As for the visual analysis, the fluctuation of written lecture content on the blackboard and the posture of lecturers are analyzed. On the other hand, the speech of lecturers is used for aural analysis. Finally, a learning-focused attention curve can be generated by fusing multiple attention models. In a sense, the values of the learning-focused attention reflect the strength of attention or semantics that the learners should pay to the blackboard lecture video and can be used for indicating the importance of the extracted lecture content at the corresponding time. Therefore, learners can easily access the blackboard lecture video with good flexibility to find what the lecture content they should understand and video frames to watch from the well-structured video. The experimental results show that the proposed method can effectively structure blackboard lecture videos and extract the lecture content with associated learning-focused attention values.