Corrosion semantic segmentation (CSS) is essential for early and accurate detection and positioning of corrosion in complex real-life scenarios. However, the unique characteristics of corrosion patterns, including the diverse forms, blurred boundaries, and intra-class heterogeneity, pose significant challenges in CSS. To address these challenges, we propose a Prototype-based Multi-dimension Sample-Adaptive Intensity Mapping with Density Sampling network (PMSAD) for CSS. PMSAD leverages nonparametric nearest prototype retrieving to enhance intra-class cohesion and inter-class separation, thereby handling the challenge of diverse forms. In PMSAD, prototypes are equally assigned to each class during training to mitigate class imbalance and capture intra-class variations. In addition, we elaborately design and implement three core components in PMSAD, including Multi-Scale Dual Attention (MSDA), Multi-dimension Sample-adaptive Intensity Mapping (MSAIM), and Density Sampling (DS). The MSDA enhances feature discrimination, facilitating robust representation learning. The end-to-end MSAIM adaptively adjusts RGB channel intensity contrasts of the input corrosion image to enhance feature robustness, counteracting the effects of uneven natural illumination. The DS is proposed for training refinement to tackle fuzzy boundaries and internal interference between corrosion classes. It focuses on high-density, high-error regions, offering refined guidance to correct intra-cluster centers and reduce inter-cluster similarity. Extensive evaluations on real-world datasets, including coarse and relabeled fine-grained dataset, validate the superior performance and generalization ability of PMSAD, achieving the new state-of-the-art performance in precise boundary delineation and accurate corrosion classification. The code is available at: https://github.com/c1oTTpD/PMSAD.