Chinese Spelling Correction is a critical task in natural language processing, yet traditional methods often suffer from weak global semantic coherence, poor domain generalization, and uncontrollable corrections. This paper proposes C2F-MAC, a Coarse-To-Fine framework integrating Large Language Model (LLM)-based domain adaptation with Multi-Agent Collaboration. In the coarse-grained phase, lightweight fine-tuning via Low-Rank Adaptation (LoRA) significantly reduces computational overhead while enhancing cross-domain terminology recognition. The fine-grained phase introduces a multi-agent mechanism driven by state machine protocols, forming a closed-loop “detect-correct-validate” workflow: agents collaboratively perform error localization, minimal-edit corrections, and necessity verification. Experiments demonstrate that C2F-MAC outperforms baseline models in cross-domain scenarios, achieving a balanced optimization of correction fidelity and resource efficiency. This work provides a robust solution for Chinese Spelling Correction with enhanced domain adaptability and controllability.