The success of deep learning models heavily depends on high-quality labeled data, yet noisy labels are inevitable in large-scale datasets. Existing methods often suffer from confirmation bias and overlook the informative value of hard but clean samples. To address these challenges, we propose Local Semantics Refinement of Adaptive Representations (LFDA), a novel framework that adaptively refines label quality by leveraging local feature consistency and representation alignment. LFDA introduces a Local Consistency Score module that evaluates the similarity among local samples in the latent space to distinguish clean from noisy labels. In addition, a confidence neighborhood is further constructed to provide local reference guidance, enabling more accurate identification and correction of noisy instances. To enhance semantic reliability, LFDA integrates a Reliability-Aware Representation Alignment (RRA) module that aligns high-confidence sample representations to implicitly refine low-confidence instances via soft supervision. Extensive experiments on both synthetic and real-world noisy datasets demonstrate that LFDA consistently outperforms state-of-the-art label noise learning methods. The results confirm its good robustness, generalization ability, and effectiveness in handling diverse and complex noise conditions.