Aiming at the limitations of existing long non-coding RNA （lncRNA） -disease association prediction models in comprehensively utilizing interaction and semantic information of heterogeneous biological networks， an lncRNA-Disease Association prediction model based on Semantic and Global dual Attention mechanism （SGALDA） was proposed. Firstly， an lncRNA-disease-microRNA （miRNA） heterogeneous network was constructed based on similarity and known associations. And a feature extraction module was designed based on message passing types to extract and fuse the neighborhood features of homogeneous and heterogeneous nodes on the network， so as to capture multi-level interactive relations on the heterogeneous network. Secondly， the heterogeneous network was decomposed into multiple semantic sub-networks based on meta-paths. And a Graph Convolutional Network （GCN） was applied on each sub-network to extract semantic features of nodes， so as to capture the high-order interactive relations on the heterogeneous network. Thirdly， a semantic and global dual attention mechanism was used to fuse semantic and neighborhood features of the nodes to obtain more representative node features. Finally， lncRNA-disease associations were reconstructed by using the inner product of lncRNA node features and disease node features. The 5-fold cross-validation results show that the Area Under Receiver Operating Characteristic curve （AUROC） of SGALDA is 0.994 5±0.000 2， and the Area Under Precision-Recall curve （AUPR） of SGALDA is 0.916 7±0.001 1， both of them are the highest among AUROCs sand AUPRs of all the comparison models. It proves SGALDA’s good prediction performance. Case studies on breast cancer and stomach cancer further prove the ability of SGALDA to identify potential lncRNA-disease associations， indicating that SGALDA has the potential to be a reliable lncRNA-disease association prediction model.