Multimodal remote-sensing classification aims to improve pixel-level land-cover recognition by exploiting complementary information from hyperspectral images and auxiliary modalities. Under limited labelled data and pronounced cross-modal discrepancies, however, existing methods often focus on feature-extraction architectures or fusion schemes, while paying less attention to whether the semantic-prior extraction branch can adapt to the current task distribution. Here we propose SGML-net (semantic-guided meta-learning network), a semantic-guided meta-learning framework that combines dual-stream feature extraction, adaptive feature fusion and a collaborative GCN-Mamba representation module. The GCN branch extracts task-relevant semantic priors from dynamic adjacency relations, whereas the Mamba branch captures long-range spatial dependencies. We further introduce a two-stage training strategy that first meta-learns task-adaptive semantic priors and then uses them as stable guidance for global semantic fusion. Experiments on the MUUFL, Houston and Berlin datasets yield average overall accuracies of 84.90%, 95.41% and 89.40%, respectively, supporting the effectiveness of SGML-net for small-sample multimodal remote-sensing classification.