The hyperspectral image (HSI) super-resolution task aims to reconstruct high-quality HSI from low-resolution hyperspectral images and high-resolution multispectral or RGB images. However, existing methods typically assume fixed spatial degradation, overlooking the complex degradation processes in real-world scenarios, which limits model generalization. To address this, we propose a novel semi-blind HSI fusion framework based on spatial degradation awareness and Spatial-Frequency enhancement. Specifically, contrastive learning is integrated into the network to extract degradation representation from low-resolution HSI with unknown spatial degradation, enabling accurate estimation and compensation of spatial distortions introduced during imaging. Moreover, a cross-domain feature enhancement strategy is employed to combine spatial details from high-resolution images with those recovered via degradation-aware processing in both spatial and frequency domains, enhancing the representation of fine-grained structures. Experimental results on multiple benchmark datasets demonstrate that the proposed method achieves superior performance in handling unknown spatial degradation.