To address the semantic ambiguity and difficulty in candidate selection caused by the diverse results generated by Large Language Models （LLMs） in intent detection and slot filling tasks， a joint intent-slot recognition method based on prompt-guided generation and tri-mutual attention mechanism was proposed， named PGT-Joint （Prompt-guided Generation and Tri-mutual attention for Joint intent-slot recognition）. First， the prompts were constructed based on domain knowledge to guide the LLM in generating multiple candidate intent-slot combinations. Second， a tri-mutual attention mechanism was designed to model the high-order semantic relationships among user questions， candidate intents， and slots explicitly. Finally， a contrastive learning ranking model was used to filter and optimize the candidate results. Experimental results on a Mini/Micro LED domain question-answering dataset show that compared with current mainstream models except Global-Locally Graph Interaction Network （GL-GIN）， such as BERT-Intent and BERT-Slot， PGT-Joint improves the intent detection accuracy by 0.4 to 3.8 percentage points and the slot filling F1 score by 4.1 to 6.6 percentage points. Notably， the most significant gain is in overall accuracy， with an improvement of 5.2 to 10.2 percentage points. In addition， ablation experimental results verify the key roles of prompt generation and tri-mutual attention mechanism in performance improvement， indicating that PGT-Joint can enhance semantic understanding capabilities in domain-specific dialogue systems effectively.