Generalized Category Discovery (GCD) presents a realistic and challenging problem in open-world learning. Given a par- tially labeled dataset, GCD aims to categorize unlabeled data by leveraging visual knowledge from the labeled data, where the unlabeled data includes both known and unknown classes. Existing methods based on parametric/non-parametric classi- fiers attempt to generate pseudo-labels/relationships for the unlabeled data to enhance representation learning. However, the lack of ground-truth labels for novel classes often leads to noisy pseudo-labels/relationships, resulting in suboptimal representation learning. This paper introduces a novel method using Nearest Neighbor Distance-aware Label Consistency sample selection. It creates class-consistent subsets for novel class sample clusters from the current GCD method, acting as “pseudo-labeled sets” to mitigate representation bias. We propose progressive supervised representation learning with selected samples to optimize the trade-off between quantity and purity in each subset. Our method is versatile and appli- cable to various GCD methods, whether parametric or non- parametric. We conducted extensive experiments on multiple generic and fine-grained image classification datasets to eval- uate the effectiveness of our approach. The results demon- strate the superiority of our method in achieving improved performance in generalized category discovery tasks.