Abstract: To address response delays and information errors and omissions caused by reliance on manual call answering and paper records in coal mine dispatching, this study investigated end-to-end intelligent voice dispatching driven by a Large Language Model (LLM) for coal mines. An intelligent voice dispatching system integrating domain fine-tuning with Retrieval-Augmented Generation (RAG) was designed. The system adopted a hierarchically decoupled architecture, used a locally deployed LLM as its decision-making core, and integrated speech recognition and synthesis technologies. By introducing an Interactive Voice Response (IVR) virtual agent mechanism, the system established a closed-loop workflow encompassing call routing, automatic work-order generation, and voice-command execution. Low-Rank Adaptation (LoRA) fine-tuning was used to adapt the model to the coal mine domain, and RAG was introduced to fundamentally prevent the generation of noncompliant instructions. A vector knowledge base based on the Coal Mine Safety Regulations was constructed as a compliance constraint, enabling end-to-end intelligent processing from voice-command recognition to execution. Field test results showed that, in coal mine environments with high noise levels and strong dialectal accents, the system's speech recognition word error rate was 0.34-0.42. With the semantic compensation capability of the fine-tuned LLM, intent recognition accuracy remained at or above 93.50%, and the average exact match rate of dispatching instructions reached 94.93%. The system operates stably in a mining area and effectively reduces dispatcher workload and the recording error rate, providing a replicable engineering paradigm for intelligent upgrading of coal mines.