随着社交媒体与电子商务平台的迅速发展，海量用户评论已成为商品与服务反馈的重要信息来源。观点目标抽取是观点挖掘中的重要任务，旨在识别评论文本中用户所评价的具体对象。该任务面临的主要挑战在于，用户表达观点时往往同时使用显式与隐式2种方式，而现有方法在隐式观点目标的识别上效果有限。为此，该文提出了一种基于双重监督对比学习的观点目标抽取方法（DCLWS），通过显式与隐式目标之间的语义关联，增强模型的判别能力。该方法融合句内上下文与跨句语义信息，构建以包含显式观点目标的评论句为锚点样本、同一类别隐式目标对应的评论句为正样本、不同类别隐式目标对应的评论句为负样本的对比学习框架，从而引导模型学习判别性更强的目标词表示与句子级语义表示。在SemEval ABSA（2014、2015和2016）挑战赛中的4个基准数据集上的实验结果表明，所提方法相较现有主流模型具有显著性能优势，F₁值最高达89.20%，且在隐式目标识别任务中，准确率提升至98.32%。结果验证了该方法在复杂语言环境中的有效性与稳健性，为观点挖掘系统在实际应用中的性能提升提供了可靠途径。

With the rapid development of social media and e-commerce platforms, massive user reviews have become a vital source of feedback on products and services. Opinion target extraction, which aims to identify the specific entities evaluated by users in review texts, represents a key task in opinion mining. A major challenge lies in the fact that users often employ both explicit and implicit expressions to convey opinions, while existing methods show limited effectiveness in recognizing implicit opinion targets. To address this issue, this paper proposes a dual-supervised contrastive learning approach for opinion target extraction (DCLWS), which enhances the model’s discriminative power by leveraging semantic relationships between explicit and implicit targets. The method integrates both intra-sentential contextual information and cross-sentential semantic cues. It constructs a contrastive learning framework that treats sentences containing explicit opinion targets as anchors, sentences corresponding to implicit targets of the same category as positive samples, and sentences of different categories as negative samples. This enables the model to learn more discriminative target word representations and sentence-level semantic embeddings. Experimental results on four benchmark datasets from the SemEval ABSA challenges (2014, 2015, and 2016) demonstrate that the proposed method achieves significant performance advantages over existing mainstream models, with the highest F1-score reaching 89.20, and improves accuracy in implicit target recognition to 98.32%. These findings confirm the effectiveness and robustness of the method in complex linguistic environments, offering a reliable pathway to enhance the performance of opinion mining systems in real-world applications.