Spoken language understanding (SLU), as a core component of task-oriented dialogue systems, aims to extract the semantic framework of user queries. In dialogue systems, the SLU component is responsible for identifying user requests and creating a semantic framework that summarizes user requests. SLU usually includes two subtasks: intent detection (ID) and slot filling (SF). ID is regarded as a semantic utterance classification problem that analyzes the semantics of utterance at the sentence level, while SF is viewed as a sequence labeling task that analyzes the semantics of utterance at the word level. Due to the close correlation between intentions and slots, mainstream works employ joint models to exploit shared knowledge across tasks. However, ID and SF are two different tasks with strong correlation, and they represent sentence-level semantic information and word-level information of utterances respectively, which means that the information of the two tasks is heterogeneous and has different granularities. This study proposes a heterogeneous interactive structure for joint ID and SF, which adequately captures the relationship between sentence-level semantic information and word-level information in heterogeneous information for two correlative tasks by adopting self-attention and graph attention networks. Different from ordinary homogeneous structures, the proposed model is a heterogeneous graph architecture containing different types of nodes and links because a heterogeneous graph involves more comprehensive information and rich semantics and can better interactively represent the information between nodes with different granularities. In addition, this study utilizes a window mechanism to accurately represent word-level embedding to better accommodate the local continuity of slot labels. Meanwhile, the study uses a pre-trained model (BERT) to analyze the effect of the proposed model using BERT. The experimental results of the proposed model on two public datasets show that the model achieves an accuracy of 97.98% and 99.11% on the ID task and an F1 score of 96.10% and 96.11% on the SF task, which are superior to the current mainstream methods.