Objective To assess the effectiveness of a two-stage deep-learning near-infrared autofluorescence (NIRAF) model for improving intraoperative parathyroid identification accuracy and to validate its utility in reducing false-positive rates and providing anatomical localization reference. Methods A total of 101 patients undergoing thyroidectomy at Fujian Medical University Union Hospital from January 2023 to December 2023 were prospectively enrolled. Under standard operating-room lighting at a fixed distance of 15 cm, a 785-nm laser NIR camera was used to acquire NIRAF images from fresh specimens’ post-resection, yielding 30,122 frames to construct the “Niraf24” dataset. The model consisted of a YOLOv8x detection network for fluorescence-signal localization and a Segment Anything Model v2 (SAM2) segmentation network for thyroid contour segmentation and background restoration. Performance was evaluated using sensitivity, precision, F1-score, false-positive rate, and intersection over union (IoU), and compared with conventional direct fluorescence reading. Results The two-stage model markedly reduced false positives: frame-level from 37.7% to 13.2% and specimen-level from 38.8% to 6.7% (both P<0.001). Precision increased from 62.3% to 86.8%, F1-score improved from 0.734 to 0.846, while sensitivity was maintained at 82.8%. The SAM2-derived thyroid contours achieved a mean IoU of (0.985±0.012), indicating highly consistent anatomical localization. The model remained robust in high-noise settings (brown adipose tissue, thermal coagulation eschar, diffuse high-fluorescence background, surgical dye contamination), effectively suppressing difficult false-positive signals. Without compromising sensitivity, the two-stage cascade approach markedly reduced false positives and improved overall performance compared to conventional interpretation methods, validating the task suitability of the “detection-first, classification-second” strategy and its feasibility for intraoperative application. Conclusion The YOLOv8-SAM2-based two-stage deep-learning cascade significantly enhances the specificity of NIRAF without compromising sensitivity, generating real-time, anatomically referenced visual information to support parathyroid preservation. This approach establishes a reproducible performance benchmark and offers a new paradigm for multicenter clinical validation and intraoperative intelligent decision-making, with the potential to reduce inadvertent parathyroidectomy and postoperative hypocalcemia.