Data distributions in real-world scenarios commonly exhibit long-tail patterns, causing deep models to suffer from head bias, where performance is biased toward head classes while tail classes are poorly recognized. A simple and effective strategy to alleviate this problem is to balance data distributions by augmenting tail-class samples. Although most methods following this strategy achieve a quantitative rebalancing, the generated samples often suffer from semantic shift and insufficient diversity, which leads to dispersed feature distributions and biased decision boundaries, thereby limiting overall model performance. To address these issues, this study proposes a sample-feature collaborative learning framework for long-tailed recognition, aiming to construct classification models that are insensitive to data distribution imbalance. At the sample level, a “magnitude augmentation” strategy based on the Fourier transform is introduced, in which amplitude shifting is employed to modify the style of augmented samples, while preserving their original semantic information. At the feature level, a “feature collapse” loss inspired by neural collapse theory is proposed to align class prototypes into an equiangular tight frame with maximum separability and to encourage features to converge toward their corresponding class prototypes, achieving maximum inter-class separation. By jointly addressing head bias from both the sample and feature perspectives, the proposed framework enhances intra-class compactness and calibrates decision boundaries. Experimental results across multiple benchmark datasets demonstrate that the proposed method significantly improves long-tailed recognition performance.