<p>Long non-coding RNAs (lncRNAs) play key regulatory roles in biological activities, making it crucial to accurately predict lncRNA–disease relationships for understanding disease pathophysiology and developing effective prevention and treatment strategies. Nevertheless, existing computational prediction methods are often limited by data sparsity and incompleteness, as well as the inadequate representation of node information. To mitigate these issues, a novel prediction model, termed DSGCNLDA, is proposed to enhance predictive performance. The proposed model integrates biological similarity features from multiple perspectives into a comprehensive similarity matrix using multi-view fusion learning. Utilizing this similarity matrix in conjunction with the adjacency matrix, a heterogeneous network of lncRNA–disease associations is constructed. Subsequently, feature extraction is conducted on the randomly masked heterogeneous network. During this process, a graph convolutional network (GCN) is employed as the encoder, and our proposed DualScope attention mechanism is introduced to more effectively capture complex topological relationships between nodes, thereby obtaining a comprehensive representation of the nodes. Finally, association prediction is made via a multi-layer perceptron (MLP). Experimental results on multiple public datasets show that DSGCNLDA performs strongly in lncRNA–disease association prediction. Ablation studies confirm its novelty, while case studies and generalization evaluations demonstrate its effectiveness in biomedical prediction tasks.</p> Graphical Abstract <p></p>