Automatic segmentation of polyp images usually results in low segmentation accuracy due to the various sizes of lesion regions and blurry boundaries. Based on these two perspectives， a novel Progressive Reduction Network （PRNet） is proposed， which first locates polyps and then gradually refines their boundaries. The network utilizes Res2Net to extract features from the lesion region and leverages the multi-scale cross-level fusion module to improve localization accuracy. By combining the attention fusion mechanism with cross-level features in this module， the network can effectively solve the issue of multi-scale lesion areas. Furthermore， PRNet combines an uncertain region processing module and a multi-scale context-aware module when restoring image resolution from top to bottom. The former gradually mines polyp edge information by setting decreasing thresholds to enhance the recognition of edge detail features， while the latter， to improve the overall representation capability of the model， further explores the inherent potential contextual semantics of lesion regions. In addition， a simple feature filtering module is designed in this algorithm to filter the valid information in the encoder features. Experimental results on the Kvasir-SEG， CVC-Clinic， and ETIS datasets show that the Dice coefficients of the algorithm reach 92.09%， 93.05%， and 74.19%， respectively. Compared with other existing polyp segmentation algorithms， PRNet outperforms them and demonstrates its superior robustness and generalization.