This study proposes an innovative method for estimating obstructive sleep apnoea (OSA) severity by integrating sleep structure and individual priori. This approach aims to overcome limitations in current OSA assessment, particularly the direct quantification of the apnoea-hypopnoea index (AHI) and the integration of multi-source information. The proposed method initially integrates multidimensional features derived from all-night nasal flow, thoracic/abdominal movements, and oxygen saturation signals. It then distinctively incorporates sleep structure parameters with clinical a priori knowledge. Subsequently, a gradient boosted regression model predicts the AHI using these multi-source features. Validation on the MESA dataset demonstrated the model′s performance, achieving R2 of 0.695, MAE of 7.46 events/h, and RMSE of 10.57 events/h. The proposed method outperformed multiple baseline models, and specifically, its R2 score showed a relative improvement of 12.46% compared to the next-best model, Random Forest, demonstrating its superiority. These results significantly surpassed those of conventional assessment methods. Feature importance analysis highlighted that parameters such as the oxygen desaturation index, N1 sleep stage percentage, and BMI were key contributors to AHI prediction. These findings indicate that the proposed method offers an effective tool for the direct, quantitative assessment of OSA severity. Furthermore, it provides a more accurate, continuous quantitative index to support clinical diagnosis and decision-making.