Knowledge Tracing (KT) aims to optimize teaching effectiveness by assessing students’ knowledge states. Existing KT methods primarily rely on learning knowledge states from historical interaction sequences but often fail to fully utilize the dataset, particularly neglecting the importance of problem embedding in KT. Although some models attempt to improve problem embeddings, they still struggle to effectively capture and model the complex relationships between problems and skills, limiting their ability to reveal the underlying information and dependencies between them. To address this challenge, we propose a Dual Hypergraph-based Question Embedding Model with Multiple Relations for Knowledge Tracing (DHGE). Specifically, we design an Explicit Relation Hypergraph (ERH) module and an Implicit Relation Hypergraph (IRH) module to capture the intricate relationships between problems and skills from both local and global perspectives. The explicit module constructs hyperedges based on annotated problem-skill mappings for local information propagation, while the implicit module captures global dependencies by establishing connections between each problem-skill pair and utilizing adaptive weights to dynamically learn problem-skill associations. Furthermore, we introduce a Question Embedding Fusion (QEF) module that integrates both local and global features of problem embeddings to generate high-quality representations. Extensive experiments demonstrate that our model outperforms existing methods, highlighting the effectiveness of high-quality problem embeddings in knowledge tracing.