Drug-Target Interaction (DTI) prediction plays a pivotal role in accelerating drug discovery and development by identifying novel interactions between drugs and targets. Most previous studies on Drug-Protein Pair (DPP) networks have primarily focused on learning their topological structures. However, two key challenges remain: the integration of topological and semantic information is often insufficient, and the representation diversity may be diminished during graph convolution operations, affecting the expressiveness of learned features. To address the above challenges, we propose a novel paradigm named Multi-view Based Heterogeneous Graph Contrastive Learning for Drug-Target Interaction Prediction (HGCML-DTI). Specifically, we initially establish a drug-protein heterogeneous graph, followed by employing a weighted Graph Convolutional Network (GCN) to derive vector representations for both drug and protein nodes. Subsequently, we individually construct the topology and semantic graphs for DPP and integrate them to form a unified public graph. A multi-channel graph neural network is employed to learn DPP representations. To preserve representation diversity and enhance discriminative ability, a multi-view contrastive learning strategy is introduced. Then, a Multilayer Perceptron (MLP) neural network is used to recognize DTI. To prove the effectiveness of this work, extensive experiments are conducted on six real-world datasets, and comparisons are made with seven competitive baselines. The results demonstrate that the proposed HGCML-DTI significantly outperforms state-of-the-art methods. This work highlights the importance of combining multi-view learning and contrastive strategies to advance the field of DTI prediction. Source codes are available at https://github.com/7A13/HGCML-DTI.