In image feature extraction, sample labels are rarely completely true and effective. This often leads to a significant decrease in the accuracy of an image classification framework. In addition, existing label recovery algorithms often must deal with a bottleneck problem in which noisy samples are difficult to reuse. Therefore, this paper proposes a subset-divided iterative projection bagging algorithm for noisy-label recovery. First, the proposed algorithm extracts small-scale subset information randomly and re. peatedly. It then integrates principal component analysis, neighbor graph regularization, K-nearest neigh. bor, and other techniques to achieve effective dimension reduction and iterative projection integration of sample images. Finally, class-label recovery is conducted by implementing the majority voting principle. This study uses common databases as experimental objects and conducts several comparisons and analy. ses of various recovery algorithms using different indicators. Experimental results show that the proposed algorithm effectively corrects the noisy labels of samples, and the classification accuracy of the default framework is improved by as much as 16. 9% and 8. 1% for the Yale B and AR databases, respective. ly. Compared with the state-of-the-art algorithm, the classification accuracy of the proposed algorithm is improved by 4. 3-4. 7%. The proposed algorithm also has good scalability and can ensure the integrity of sample data.