In the online recruitment domain, efficient and accurate resume-job matching is essential for optimizing talent selection and work allocation. However, existing deep contrastive learning methods still face two major challenges: the symmetry assumption restricts the differentiation of resume and job representations, reducing semantic distinction; random negative sampling introduces low-value samples, weakening the model of ability to distinguish similar matches. To address these issues, we propose Dynamic Asymmetric Contrastive Learning (DACL), which improves representation learning and negative sampling quality through asymmetric contrastive learning and adaptive hard negative mining. Our approach introduces a bidirectional temperature regulation mechanism to independently optimize resume-to-job and job-to-resume matching separately, mitigating gradient conflicts and improving adaptability. Additionally, we introduce a dynamic hard negative selection mechanism, leveraging both semantic and structural features to identify high-confusion negatives, improving model robustness. Experiments results on real-world datasets demonstrate that DACL significantly improves matching accuracy and retrieval efficiency, providing a generalizable and scalable optimization framework for resume-job matching.