Multi-intent Spoken Language Understanding (Multi-intent SLU) can extract multiple intents in a single utterance, gaining increasing attention. Nevertheless, current multi-intent SLU approaches still heavily rely on large amounts of annotated multi-intent SLU data, which makes it hard to be satisfied in real-world scenarios without sufficient data. Motivated by this, we introduce a novel decoupled pre-training framework (DPF) to address the data-scarcity problem, achieving to leverage of abundant multi-intent-free SLU data to enhance multi-intent SLU. Specifically, DPF first decouples the multi-intent SLU task into two abilities: (1) task-agnostic ability to locate the task-agnostic slot entity span and (2) task-specific ability to predict the task-specific slot and intent labels simultaneously. The key insight of DPF is that such decomposition allows us to design a two-stage decoupled pre-training procedure to enhance both task-agnostic ability and task-specific ability with abundant multi-intent-free SLU data (i.e., NER and single-intent SLU data), respectively. Experimental results on two standard benchmarks (e.g., MixATIS and MixSNIPS) demonstrate the effectiveness of DPF by achieving superior performance. In addition, extensive analyses reveal that utilizing the multi-intent-free data can effectively enhance multi-intent SLU.