Currently, transformer fault diagnosis primarily relies on the subjective judgment of maintenance personnel, which entails significant human effort and expertise. Moreover, unstructured text data—such as historical defect logs and maintenance records—are not effectively leveraged for the intelligent generation of maintenance strategies, hindering accurate status evaluation and proactive risk management. This paper proposes TransQwen, a domain-adapted LLM tailored for transformer fault diagnosis and maintenance strategy generation. Built upon the Qwen-7B-Chat architecture, TransQwen is fine-tuned on a domain-specific corpus encompassing transformer fault cases aligned with technical standards and operational procedures. It integrates DoRA for efficient parameter adaptation and RoPE to enhance positional encoding during training. The model is evaluated in three core tasks: fault type classification, fault severity grading, and strategy generation. The results show significant improvements—over 10 percentage point gains in standard conditions and up to 30 percentage points in F1 score under extreme low-sample settings (e.g., 100 samples), demonstrating robust generalization. In the maintenance strategy generation experiment, all the evaluation results of the TransQwen model reached the optimal. Through a knowledge-driven approach, the model can perform question-and-answer tasks involving professional knowledge in the power vertical field, and customize and generate accurate maintenance strategies for specific fault scenarios.