Objective To construct an artificial intelligence surgical workflow reconstruction system integrating instance segmentation and multi-object tracking to realize automatic structural analysis and objective quality evaluation of surgical videos. Methods A total of 20 videos of laparoscopic radical gastrectomy performed between January 2022 and December 2023 at the Department of Gastrointestinal Surgery Xuzhou Central Hospital; the Department of General Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School; and the Department of General Surgery, the Fourth Affiliated Hospital of Soochow University were retrospectively analyzed. A total of 7569 images annotated with 11 types of surgical instruments and intraoperative bleeding areas were extracted and divided into training and validation datasets at a ratio of 8:2. Transfer learning was performed based on the YOLO11L-seg model, and the ByteTrack algorithm was integrated for temporal tracking of surgical instruments. Model performance was verified from three dimensions: internal static image validation, external independent dynamic surgical video validation, and blind evaluation by clinical surgeons. Evaluation metrics included mAP@0.5, mask mAP@0.5, recognition accuracy, and F1/Dice coefficient. A surgical procedure heatmap was generated based on the temporal frequency of instrument usage to quantitatively distinguish different surgical procedures and locate key operative steps. Results In the internal static validation, the box mAP@0.5 and mask mAP@0.5 of the model reached 83.5% and 82.8%, respectively. In the external dynamic video test set, the overall recognition accuracy was 92.2% with an F1/Dice coefficient of 0.878, and the recognition accuracy of intraoperative bleeding was 100%. The surgical heatmap could completely distinguish three radical gastrectomy procedures including distal, total and proximal gastrectomy, and the localization accuracy of key operative steps exceeded 90%. The intelligent review time of a single surgical video was reduced by approximately 70% compared with manual review. Blind evaluation by three senior surgeons demonstrated an average accuracy of 100% in instrument identification, bleeding detection and surgical procedure classification. Conclusion The instrument recognition and tracking scheme based on YOLO11L-seg and ByteTrack can accurately reconstruct the temporal workflow of laparoscopic radical gastrectomy. The surgical heatmap can intuitively quantify surgical operation characteristics, providing a feasible technical strategy for automatic and standardized quality control of minimally invasive surgery.