Abstract The integration of artificial intelligence (AI) into surgical navigation represents a pivotal advancement in modern operative medicine. In endoscopic thyroidectomy, safeguarding the recurrent laryngeal nerve (RLN) is of critical importance due to its vulnerability to iatrogenic injury, which affects 3–8% of cases and can lead to serious complications such as vocal cord paralysis. However, existing intraoperative nerve monitoring (IONM) technologies are limited by high costs, operator dependence, and discontinuous signal acquisition. To address the lack of large-scale, annotated datasets essential for training robust deep learning models in real-world surgical settings, we present ThyRLN-PUMCH, the first comprehensive in vivo dataset dedicated to RLN identification in endoscopic thyroid surgery. This dataset comprises 18,178 pixel-level annotated frames from 28 clinically diverse surgical cases. Annotations were performed and validated by board-certified endocrine surgeons through a multi-stage quality control process. We benchmarked two segmentation models to verify their practicability and proved the dataset’s capacity to support high-precision RLN segmentation tasks. ThyRLN-PUMCH fills a critical gap in AI assisted head and neck surgery by offering temporally continuous, clinically representative images and annotations. It provides a robust foundation for developing AI-based intraoperative navigation tools aimed at enhancing surgical safety, education, and efficiency in head and neck surgery. Similar content being viewed by others Data availability The data that support the findings of this study are openly available in Kaggle at https://doi.org/10.34740/kaggle/dsv/1201463023. A detailed description of the data files and structure is provided in the Data Record section of this paper. Code availability The code for the models related to the technical validation of this dataset is publicly available at: https://github.com/AriaCui/mmsegmentation?tab=readme-ov-file#. References Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3), 209–249 (2021). Boucai, L., Zafereo, M. & Cabanillas, M.