To address the issues of overlapping relation representations and incorrect relation predictions in Zero-Shot Relation Extraction （ZSRE） caused by similar entities or relations， a Dual Contrastive Learning-based Zero-Shot Relation Extraction （DCL-ZSRE） model was proposed. Firstly， both instances and relation descriptions were encoded using pre-trained encoders to obtain their vector representations. Secondly， a dual contrastive learning was designed to enhance the distinguishability of relation representations： Instance-level Contrastive Learning （ICL） was used to learn mutual information between instances， then the representations of instances and relation descriptions were concatenated； and Matching-level Contrastive Learning （MCL） was applied to learn the associations between instances and relation descriptions， thereby resolving the problem of overlapping relation representations. Finally， the learned representations from contrastive learning were utilized in the classification module to predict unseen relations. Experimental results on FewRel and Wiki-ZSL datasets demonstrate that DCL-ZSRE model significantly outperforms eight state-of-the-art models in terms of precision， recall， and F1-score， especially with the large number of unseen relation categories. With 15 unseen relations， DCL-ZSRE achieves improvements of 4.76， 4.63 and 4.69 percentage points in three indicators over EMMA （Efficient Multi-grained Matching Approach） model on FewRel dataset， and also achieves improvements of 1.32， 2.20 and 1.76 percentage points on Wiki-ZSL dataset. These results confirm that DCL-ZSRE model effectively distinguishes overlapping relation representations， establishing an efficient and robust approach for ZSRE.