Abstract: 【Purpose/significance】Scientific diagrams serve as an intuitive and structured way to present complex research findings. However, existing research on scientific literature analysis has primarily focused on textual objects, with insufficient exploration of dia⁃ gram interpretation. The flowcharts and framework diagrams in this paper's scientific diagrams are the research objects, aiming to ex⁃ plore intelligent understanding methods applicable to scientific figures.【Method/process】This study proposes a two-stage approach for scientific diagram understanding. In the first stage, coarse bounding boxes are generated to achieve preliminary localization of dia⁃ gram modules. In the second stage, a visual attention point simulation strategy that integrates both global and local features is em⁃ ployed to construct positive and negative sample points, thereby optimizing model performance and enabling modular parsing and se⁃ mantic alignment.【Result/conclusion】Experiments on a manually annotated real dataset of 13,000 randomly generated flowchart data show that the method proposed in this paper outperforms existing models in both mIoU and mrIoU metrics. The overall performance mrIoU is as high as 0.694. Even in complex flowchart scenarios, the mrIoU reaches 0.608, which is more than 0.22 higher than other models.【Innovation/limitation】This method provides a systematic solution for interactive flowchart diagram understanding and estab⁃ lishes a theoretical foundation for multimodal interactive reading technologies