<p>Multi-resolution analysis has been widely used to alleviate the limitations of single-resolution approaches in polyphonic sound event detection. However, existing multi-resolution methods usually rely on category-agnostic fusion and simple interpolation-based alignment, which cannot effectively exploit category-dependent resolution preference and may introduce temporal inconsistency across resolutions. To address these issues, this paper proposes a category-guided multi-resolution fusion approach(CGMRF). Specifically, a Category-Guided Fusion (CGF) method is designed to learn category-dependent fusion weights over different resolutions, enabling more targeted and interpretable use of complementary time-frequency information. In addition, a Frame-Overlap Weighted Alignment (FOWA) method is introduced to align multi-resolution outputs by explicitly modeling the overlap relationship between adjacent STFT frames, thereby better preserving temporal continuity than linear interpolation. Overlap averaging is further employed as a lightweight post-processing step to suppress prediction jitter and improve temporal smoothness. Experiments on the TUT Sound Events 2017 and DESED datasets show that the proposed approach generally outperforms averaging fusion and several attention-based fusion baselines. On TUT Sound Events 2017, CGF improves the F1 score by 1.6% and reduces the error rate by 3.0% compared with averaging fusion, while introducing only negligible additional parameters. On DESED, CGMRF also achieves competitive improvements in PSDS, Event-F1, Segment-F1(Seg-F1), and ER. Moreover, the learned category-wise fusion weights are generally consistent with the relative detection capabilities of different resolutions, confirming the effectiveness and interpretability of CGMRF.</p>