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The application and impact of image-text retrieval in industry are multifaceted, as it can help improve research and development efficiency, promote technological innovation, and enhance product quality and competitiveness. Currently, the emphasis of image-text retrieval models is on improving retrieval accuracy. With the rapid development of technology and data, the continuous application of deep learning and large-scale model techniques has gradually highlighted the issue of retrieval speed in image-text retrieval. To address the current limitations in retrieval speed and high computational requirements, a hierarchical clustering-based image-text retrieval model has been proposed. This method adopts a cross-modal hashing approach with evident retrieval effectiveness and applies deep clustering algorithms to classify the data to be retrieved, thereby reducing the retrieval scope and improving retrieval speed. Experimental results indicate that the hierarchical clustering-based image-text retrieval model significantly enhances retrieval speed while maintaining retrieval accuracy, enabling engineering personnel to obtain satisfactory retrieval results more quickly.