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Microscopic hyperspectral image (MHSI) provides rich spectral information that enables the detection of subtle biochemical variations in tissue, making it a powerful modality for computational pathology. However, hyperspectral data exhibits substantial spectral redundancy, which may obscure critical discriminative cues. Current approaches rarely focus on identifying informative spectral components, thus failing to leverage the spectral invariance of pathological samples. Moreover, large-scale precise annotations are difficult to obtain, and textual description for MHSI remains largely unavailable, limiting supervised learning. To address these challenges, we propose a novel \textbf{Lo}w-rank \textbf{T}ext-guided \textbf{S}pectral learning \textbf{Net}work for semi-supervised MHSI segmentation, named as \textbf{LoTS-Net}. Specifically, we introduce a text-guided spectral channel selection mechanism to extract refined low-rank spectral representations and incorporate textual semantics to enable interpretable and redundancy-aware channel selection. Then, we leverage intrinsic spectral similarity across samples by constructing a spectral feature container that retrieves correlated prototypes to improve spectral-spatial fusion. This container mitigates noise during the interaction between labeled and unlabeled samples. To support future research, we construct the first microscopic hyperspectral vision-language benchmark and conduct comprehensive evaluations. Experimental results demonstrate that our method significantly outperforms the state-of-the-art methods, particularly under the challenging 5\% labeled data setting. Code is available at \url{https://github.com/ECNU-MultiDimLab/LoTS-Net}.