Explainable deep learning reveals a stagewise temporal deterioration from skin tone to contour to texture in Chinese women's skin aging.

Skin aging is a continuous and progressive physiological process characterized by gradual structural and functional changes. Early identification and accurate assessment of aging-related facial features are essential for developing targeted skincare strategies. This study aimed to establish a deep learning-based framework combined with Shapley Additive Explanations (SHAP) analysis to classify facial aging stages in Chinese women and provide interpretable evidence for personalized skincare. A total of 300 Chinese women aged 18-60 years were included. Standardized facial images were acquired using the VISIA imaging system. Four dimensions, including tone, contour, texture, and porphyrin, were evaluated, and 23 facial indicators were extracted. A hybrid multimodal deep learning classification model was developed by integrating facial image data with quantitative facial aging indicators. Five-fold stratified cross-validation was performed to evaluate model performance, with Top-2 accuracy used as the primary evaluation metric. SHAP analysis was further applied to assess the contribution weights of different facial aging indicators to model predictions. Based on the fitted quantitative facial aging indicators, a six-class facial aging classification model was established. The model demonstrated stable convergence and achieved a highest validation Top-2 accuracy of 85.0%. The confusion matrix showed that classification errors occurred primarily between adjacent aging stages. SHAP analysis revealed a progressive aging pattern characterized by early changes in skin color, middle-stage changes in facial contour, and later deterioration in skin texture. This study demonstrates that integrating deep learning with SHAP analysis can effectively classify facial aging stages in Chinese women while providing interpretable insights into the key features driving aging-related predictions.