Drug-target interaction (DTI) prediction is crucial for drug discovery, as it accelerates candidate screening and reduces development costs. However, existing computational methods are often limited to a single perspective and cannot simultaneously consider the biological information and complex associations of drugs and targets. Although multimodal data have been introduced, the complementarity and interaction of multi-source information remain underutilized, making efficient multi-view feature fusion a key challenge. In this paper, we propose a DTI prediction framework based on multi-view feature fusion and contrastive learning, named MFCL-DTI. It integrates sequence feature as well as structural and semantic information of heterogeneous graph. A multi-view adaptive fusion module facilitates cross-view feature fusion, while multi-view contrastive learning enhances feature representation. Experimental results demonstrate that MFCL-DTI outperforms existing methods, validating its effectiveness in DTI prediction.