Semi-supervised subspace clustering has achieved promising results in hyperspectral image (HSI) analysis by leveraging spectral structure with limited labels. However, most existing methods suffer from two key limitations. First, they separate feature learning and clustering, preventing joint optimization. Second, they rely on explicit self-representation matrices with O(n²) complexity, leading to significant memory and computation overhead. To address these issues, we propose an end-to-end semi-supervised deep subspace clustering framework tailored for HSI data. It unifies representation learning and clustering into a single trainable architecture, preserving contextual and subspace structures without computing large affinity matrices. The framework integrates cross-entropy loss, supervised contrastive learning, label-guided subspace modeling, and pseudo-label-based consistency regularization, enabling effective use of scarce labeled and abundant unlabeled data. Experiments on standard HSI datasets demonstrate that our method consistently outperforms existing unsupervised and semi-supervised clustering approaches under low-label regimes, illustrating the practical potential of semi-supervised deep subspace clustering for real-world HSI interpretation.