With the rapid development of intelligent driving technology, driver attention monitoring has become a crucial research focus for ensuring road safety. Traditional distraction detection methods rely on discrete behavior classification, which fails to capture the continuous variation of driver attention and often neglects individual differences in physiological structures and gaze habits.To address these limitations, this paper proposes a gaze estimation-based distraction detection method that continuously identifies distraction states by evaluating the deviation of gaze Euler angles from a predefined safe attention region. The proposed model adopts a ResNeXt-Transformer hybrid architecture that integrates local detail extraction with global dependency modeling, and incorporates a feature enhancement module to strengthen task-relevant representations and improve gaze feature extraction. Furthermore, a multi-level personalized calibration mechanism performs bias compensation at both the feature and output layers, enabling rapid individual adaptation under few-shot conditions.Experimental results on the MPIIGaze dataset demonstrate that the proposed method significantly outperforms existing approaches in cross-user gaze estimation, reducing the average angular error to 2.94°. Visualization and validation in real-world driving scenarios further confirm that the method exhibits strong interpretability and high practical applicability.