Generative methods have recently gained traction in biological and chemical named entity recognition for their ability to overcome tagging limitations and better capture entity-rich contexts. However, under a few-shot environment, they struggle with the scarcity of annotated data and the structural complexity of biological and chemical entities—particularly nested and discontinuous ones—leading to incorrect recognition and error propagation during generation. To address these challenges, we propose SCoNE, a Self-Correcting and Noise-Augmented Method for Complex Biological and Chemical Named Entity Recognition. Specifically, we introduce a Noise Augmentation Module to enhance training diversity and guide the model to better learn complex entity structures. Besides, we design a Confidence-based Self-Correction Module that identifies low-confidence outputs and revises them to improve generation robustness. Benefiting from these designs, our method outperforms the baselines by 1.80 and 2.73 F1-score on the CHEMDNER and microbial ecology dataset Florilege, highlighting its effectiveness in biological and chemical named entity recognition.