This study proposes a large language model, Corr-Lora-RAG, designed to address the complexity and uncertainty inherent in corrosion data. A dedicated corrosion knowledge database (CKD) was constructed, and dataset generation code was provided to enhance the model’s reproducibility and adaptability. Based on the Qwen2.5-7B model, the Corr-Lora model was developed by integrating prompt engineering and low-rank adaptation (LoRA) supervised fine-tuning (SFT) techniques, thereby improving the understanding and expression of domain-specific knowledge in the field of corrosion. Furthermore, the Corr-Lora-RAG model was built using retrieval-augmented generation (RAG) technology, enabling dynamic access to external knowledge. Experimental results demonstrate that the proposed model outperforms baseline models in terms of accuracy, completeness, and domain relevance, and exhibits knowledge generation capabilities comparable to those of large-scale language models under limited computational resources. This approach provides an intelligent solution for corrosion risk assessment, standards compliance analysis, and protective strategy formulation, and offers a valuable reference for the development of specialized language models in other engineering fields.