The assessment of gingival inflammation surface features mainly depends on subjective judgment and lacks quantifiable and reproducible indicators. Therefore, it is a need to acquire objective identification information for accurate monitoring and diagnosis of gingival inflammation. This study aims to develop an automated method combining intraoral scanning (IOS) and deep learning algorithms to identify the surface features of gingival inflammation and evaluate its accuracy and correlation with clinical indicators.The study included the periodontal probing data and intraoral scan images of 120 patients with periodontitis. The deep learning model GC-U-Net was used to automatically segment and identify the gingival inflammation regions. The performance of the model was evaluated by the Dice coefficient (Dice), intersection over union (IoU), and pixel accuracy (PA), and the correlation between the identification performance and the periodontal examination index was analysed.The GC-U-Net model showed high recognition accuracy, with a Dice of 77.8%, an IoU of 65.4%, and a PA of 93.7%. This model demonstrated a strong positive correlation with the sulcus bleeding index (SBI; r = 0.836, P < .001), a moderately strong positive correlation with the bleeding index (BI; r = 0.618, P < .001), and a negative correlation with the probe depth (PD; r = - 0.425, P < .001).The study has successfully developed an automatic identification method for surface characteristics of gingival inflammation based on deep learning and IOS technology, providing a standardised and automated auxiliary tool for clinical gingival inflammation examination with high accuracy and significant correlation with clinical indicators.This method can reduce subjective judgment in the clinical assessment of gingival inflammation, improve the consistency and reliability of identification, and play an important auxiliary role in clinical diagnosis and treatment planning.Copyright © 2025 The Authors. Published by Elsevier Inc. All rights reserved.