Diverse degradation types and the difficulty of detail recovery make real-world image super-resolution challenging, with existing methods still struggling with structural preservation and semantic consistency. This study proposes a semantic-aware interactive diffusion method for image super-resolution reconstruction (SISRM) method. Semantic segmentation information is introduced as prior knowledge, enhancing structural understanding and providing semantic guidance during reconstruction. Specifically, a segmentation-aware prompt extractor is designed and trained to efficiently obtain segmentation mask embeddings and semantic labels from degraded low-resolution images using a segmentation mask encoder and a label text generator. An interactive text-to-image controller is then introduced, integrating a segmentation-guided cross-attention module with a trainable image encoder. The diffusion process is guided under multi-modal semantic conditions to enhance local detail and global structure awareness. Finally, a mask feature fusion mechanism is proposed to mitigate the mismatch between local conditional control and the global latent distribution, improving the consistency and visual quality of the generated images. Experimental results on the DIV2K-Val and RealSR datasets show that the proposed method achieves the highest scores of 0.6121 and 0.7274 in no-reference and cross-modal image quality assessment, respectively. These results demonstrate notable improvements in detail restoration, semantic consistency, and overall perceptual quality.