Identifying Drug-Target Interactions （DTI） is a crucial step in drug repurposing and novel drug discovery. Currently， many sequence-based computational methods have been widely used for DTI prediction. However， previous sequence-based studies typically focus solely on the sequence itself for feature extraction， neglecting heterogeneous information networks such as drug-drug interaction networks and drug-target interaction networks. Therefore， a novel method for DTI prediction based on sequence and multi-view networks was proposed， namely SMN-DTI （prediction of Drug-Target Interactions based on Sequence and Multi-view Networks）. The Variational AutoEncoder （VAE） was used to learn the embedding matrices of drug SMILES （Simplified Molecular-Input Line-Entry System） strings and target amino acid sequences in this method. Subsequently， a Heterogeneous graph Attention Network （HAN） with two-level attention mechanism was used to aggregate information from different neighbors of drugs or targets in the networks from both node and semantic perspectives， obtaining the final embeddings. Two benchmark datasets widely used for DTI prediction， Hetero-seq-A and Hetero-seq-B， were used to evaluate SMN-DTI and the baseline methods. The results show that SMN-DTI achieves the best performance in Area Under the receiver operating Characteristic curve （AUC） and the Area Under the Precision-Recall curve （AUPR） under three different positive-and-negative sample ratios. It can be seen that SMN-DTI outperforms current mainstream advanced prediction methods.