This study considers slot filling as a crucial component of task-oriented dialogue systems, which serves downstream tasks by identifying specific slot entities in utterances. However, in a specific domain, it necessitates a large amount of labeled data, which is costly to collect. In this context, cross-domain slot filling emerges and efficiently addresses the issue of data scarcity through transfer learning. However, existing methods overlook the dependencies between slot types in utterances, leading to the suboptimal performance of existing models when transferring to new domains. To address this issue, a cross-domain slot filling method based on slot dependency modeling is proposed in this study. Leveraging the prompt learning approach based on generative pre-trained models, a prompt template integrating slot dependency information is designed, establishing implicit dependency relationships between different slot types and fully exploiting the predictive performance of slot entities in the pre-trained model. Furthermore, to enhance the semantic dependencies between slot types, slot entities, and utterance texts, discourse filling subtask is introduced in this study to strengthen the inherent connections between utterances and slot entities through reverse filling. Transfer experiments across multiple domains demonstrate significant performance improvements in zero-shot and few-shot settings achieved by the proposed model. Additionally, a detailed analysis of the main structures in the model and ablation experiments are conducted in this study to further validate the necessity of each part of the model.