Early diagnosis of pediatric adenoidal hypertrophy is crucial for timely identification of respiratory obstructions, thereby preventing declines in sleep quality that can lead to impaired growth, development, and cognitive function. Currently, the most common clinical diagnostic methods involve nasopharyngeal endoscopy and X-ray imaging assessments, which are time-consuming, labor-intensive, and costly. This paper presents an intelligent diagnostic model based on natural light facial images, employing a state space model (SSM). The model combines the local feature extraction capability of convolutional layers with the SSM’s ability to capture long-range dependencies for facial feature extraction. Furthermore, it incorporates a feature fusion strategy to enhance the features of facial images. Experimentation on a self-constructed dataset of adenoid facies images demonstrated that the model achieves an accuracy of 86.55% and an F1-score of 86.21%. The data and code are available at https://github.com/hejinrong/AF-SSM .