Knowledge tracing (KT) plays a key role in adaptive learning, yet traditional recurrent neural network-based methods often struggle with sparse data and overlook relationships between knowledge points. To address these limitations, this paper proposes a novel knowledge tracing model (KGS-KT) that integrates knowledge graphs, graph convolutional networks (GCNs), and self-supervised learning (SSL). Our approach constructs a knowledge graph from learners' performance data to capture inherent relationships among knowledge points and employs GCNs to generate comprehensive knowledge point embeddings. In addition, two SSL tasks--node attribute prediction and edge relation prediction--are introduced. These tasks enhance feature representation by reconstructing masked node attributes and inferring missing connections, while also providing deeper insight into the role of structured dependencies. This strengthens the model's robustness against data sparsity. Experimental results show that our approach outperforms existing methods, demonstrating the effectiveness of combining structured knowledge and SSL in KT.