Fine-grained negative emotion classification suffers from subjective annotation noise and semantically ambiguous hard samples, which severely degrades model performance. Most existing methods cannot distinguish valid hard samples from noise and lack strategies to improve hard sample learnability. To address these issues, we propose a Noise Filtering and Difficulty-adaptive Hard Sample Augmentation (NF-DHSA) framework for fine-grained negative emotion classification. It first mines high-risk candidates using a hybrid score of normalized loss and predictive entropy, then applies a multi-view logic-guided filtering mechanism to reliably separate noise from valid hard samples. Further, difficulty-adaptive dynamic augmentation generates moderate-difficulty, label-stable augmented samples, and prototype-guided dual-granularity contrastive learning aligns original-augmented pairs while strengthening global category boundaries. The results reported on three benchmark datasets show that NF-DHSA outperforms the baseline methods overall, with Macro-F1 scores improved by 1.25%, 2.47%, and 3.53% on the VIRUS, NLPCC, and COVID datasets, respectively.