The fuzzy K-nearest neighbors (FKNN), a renowned fuzzy classifier, has attained commendable performance. We introduce adaptive fuzzy contribution classification (AFCC), a novel fuzzy classifier that diverges from FKNN’s reliance on majority voting. Instead, AFCC employs an improved category contribution rate (ICCR) to delineate the hierarchical relationship between samples and categories. ICCRs quantify the contribution of a sample in reconstructing all categories, exhibiting resilience to noise. In the presence of noisy samples, AFCC adjusts all ICCRs concurrently, preserving their differentials and thus the hierarchical relationship between samples and categories. In AFCC, a precise subordination degree for each sample across all categories is determined, bridging the gap between samples and their respective categories. With this subordination degree established, the decision subordination degree for each test sample toward all categories can be derived using relevant samples. The category with the highest decision subordination degree is then selected. Notably, AFCC operates as a parameter-free classifier. Experiments have been extensively conducted on various datasets, including the Yale, Olivetti Research Laboratory (ORL), and Aleix Martinez and Robert Benavente (AR) face databases, as well as the Center for Pattern Recognition and Machine Intelligence (CENPARMI) handwritten numeral database. The outcomes unequivocally demonstrate that the recognition performance of AFCC surpasses that of other methods. Notably, in the AR face database experiments, AFCC achieves remarkable recognition accuracies, reaching 99% for images exhibiting expression variations and an impressive 100% accuracy for images subjected to illumination changes, showcasing its potential for applications in areas requiring robust and efficient pattern recognition, such as facial recognition, handwriting analysis, and other image-based classification tasks.