Ultraviolet radiation is a primary external factor contributing to skin photoaging, as it induces cellular deoxyribonucleic acid damage and collagen degeneration, thereby accelerating skin aging and increasing the risk of skin cancer. Currently, skin aging assessment mainly relies on dermatologists’ empirical judgment, which is inherently subjective and inefficient. To address these limitations, this study proposes a deep learning–driven intelligent skin aging classification method based on high-frequency ultrasound images. The proposed model employs efficient network version 2 (EfficientNetV2) as the backbone network and introduces the Gaussian error linear unit (GELU) activation function and layer normalization to enhance nonlinear feature representation and training stability. In addition, a global-aware temporal hierarchical network is integrated to enable efficient multi-scale feature extraction of skin tissue. A dual enhancement attention mechanism, combining parallel channel–spatial attention and squeeze-and-excitation modules, is further designed to improve the model’s sensitivity to key aging-related regions. Moreover, a multi-scale path dropout regularization strategy is adopted to effectively alleviate overfitting. Experiments conducted on a facial high-frequency ultrasound dataset collected from subjects aged 25~55 years demonstrate that the proposed method achieves an accuracy of 87.66%, a precision of 88.27%, a recall of 87.66%, an F1 score of 87.80%, and a specificity of 97.94%, consistently outperforming existing mainstream models. These results indicate that the proposed approach enables high-precision identification of skin aging levels and provides an efficient and objective auxiliary diagnostic tool for skincare, anti-aging treatment, and the prevention of photoaging-related skin diseases.