Spoken language understanding is a key task in task-based dialogue systems, mainly composed of two sub-tasks: slot filling and intent detection. Currently, the mainstream method is to jointly model slot filling and intent detection. Although this method has achieved good results in both slot filling and intent detection, there are still issues with error propagation in the interaction process between intent detection and slot filling in joint modeling, as well as the incorrect correspondence between multi-intent information and slot information in multi-intent scenarios. In response to these problems, this study proposes a joint model for multi-intent detection and slot filling based on graph attention networks (WISM). The WISM established a word-level one-to-one mapping relationship between fine-grained intentions and slots to correct incorrect correspondence between multi-intent information and slots. By constructing an interaction graph of word-intent-semantic slots and utilizing a fine-grained graph attention network to establish bidirectional connections between the two tasks, the problem of error propagation during the interaction process can be reduced. Experimental results on the MixSINPS and MixATIS datasets showed that, compared with the latest existing models, WISM has improved semantic accuracy by 2.58% and 3.53%, respectively. This model not only improves accuracy but also verifies the one-to-one correspondence between multi-intent and semantic slots.