<p>Partial multi-label learning (PML) aims to train predictive models from ambiguously annotated data where each sample is associated with a set of candidate labels containing both true and noisy ones. Existing methods mainly rely on label disambiguation or feature correction. However, disambiguation-based methods often depend on restrictive structural assumptions, while feature correction methods only refine feature representations and leave the decision-making process unchanged, thus limiting adaptability to complex noise patterns. To address these limitations, we propose ARCFNP, an adaptive rule-correcting fuzzy neural network for PML. Specifically, ARCFNP employs a multi-label Takagi-Sugeno-Kang (TSK) fuzzy neural network as the base classifier and incorporates an adaptive rule-correcting module that corrects the antecedent-consequent mapping of the multi-label TSK fuzzy neural network. Moreover, a bi-level meta-optimization strategy uses noisy training data to update the base learner and a small clean validation set to guide the rule-correcting parameters. Experiments over multiple datasets show that ARCFNP achieves the best average ranks on all five evaluation metrics, with ranks of 1.0, 1.7, 2.2, 1.1, and 1.5 for AP, HL, OE, RL, and CV, respectively. Additional analyses demonstrate its robustness under complex instance-dependent noise, stable optimization behavior, approximately linear scaling with the label-space size, and interpretable rule-level corrections for PML.</p>