Large Language Models (LLMs) have demonstrated impressive capabilities in language generation and general task performance.However, their application to spoken language understanding (SLU) remains challenging-particularly for token-level tasks, where the autoregressive nature of LLMs often leads to misalignment issues.They also struggle to capture nuanced interrelations in semanticlevel tasks through direct fine-tuning alone.To address these challenges, we propose the Entitylevel Language Model (ECLM) framework, which reformulates slot-filling as an entity recognition task and introduces a novel concept, Chain of Intent, to enable step-by-step multiintent recognition.Experimental results show that ECLM significantly outperforms strong baselines such as Uni-MIS, achieving gains of 3.7% on MixATIS and 3.1% on MixSNIPS.Compared to standard supervised fine-tuning of LLMs, ECLM further achieves improvements of 8.5% and 21.2% on these datasets, respectively.Our code is available at https: //github.com/SJY8460/ECLM.