Seismic impedance inversion is essential for reservoir characterization but remains challenging in complex geological environments due to the inherent limitations of conventional methods. This study proposes a hybrid deep learning framework integrating a convolutional neural network (CNN), a graph attention network (GAT), and a gradient boosting decision tree (GBDT) to achieve high-resolution impedance inversion. The CNN extracts local structural features from seismic waveforms, the GAT captures long-range geological dependencies through self-attention between traces, and the GBDT performs robust non-linear regression for final prediction. Extensive evaluations on synthetic and field datasets demonstrate that the method achieves a root mean square error of 285 m/s·g/cm3 on the Society of Exploration Geophysicists salt model, representing a 15.2% improvement over XGBoost and a 32.1% improvement over sparse spike inversion. The framework performs particularly well in complex regions, achieving a 22.7% error reduction at salt boundaries and a thin-bed detection rate of 92% for layers exceeding 4 m in thickness. Statistical uncertainty quantification indicates 94.2% coverage of true impedance values within 95% confidence intervals. In practical applications, the method reduces interpretation time by 40% while maintaining reservoir thickness prediction errors within ± 3 m, demonstrating strong robustness and operational value for seismic interpretation.