Shot boundary detection （SBD） of lecture video is of great significance to teaching evaluation （TE）. This paper proposes a new SBD method to address the problems that the changes of visual information of lecture videos are subtle， only boundary information is insufficient and the detection results of current methods are not beneficial to TE. The proposed method is based on the vision and text representation learning features with attention mechanism. Firstly， the hierarchical vision transformer （HViT） model is proposed to learn the visual features from the regions of interest （ROI） such as screen projection， teacher and students. Secondly， the hierarchical text transformer （HTT） model is proposed to learn features concerned in teaching evaluation from the speech and screen text. Finally， the loss function is constructed with binary cross entropies of the shot classification and boundary detection jointly. Experimental results on CLShots dataset show that the average precision， recall， F₁-score and mean intersection over union of our method are higher by 23.3%， 22.4%， 22% and 35.7% compared with those of the state-of-art method of SBLV， while higher by 13.8%，14.5%，14.3% and 21.3% compared with those of the method of TransNet V2.