The random placement of books in unmanned libraries poses challenges to the accuracy and adaptability of existing spine segmentation algorithms, while large-scale matching algorithms are hindered by poor real-time performance and low accuracy. In order to solve those issues, a book spine segmentation and matching algorithm was proposed, where the SAM(segment anything model) segmentation model was combined with a multi-depth feature cascade matching approach to ensure accurate spine segmentation and precise matching. Firstly, the SAM model was employed for segmenting book images, and a vision Transformer (ViT) was used to efficiently extract features. Local and global information was captured through the self-attention mechanism to enable the generation of high-quality multi-scale spine feature maps for effective segmentation. Secondly, the proposed feature hemisection network (FHNet) was utilized to extract low-frequency, high-frequency, and fused features, which respectively focused on color, texture and contour information to enhance spine distinctiveness. Additionally, the FAISS(facebook AI similarity search) framework was incorporated for cascade matching, where color features, FHNet features and VGG(visual geometry group) features were integrated to improve real-time performance. It has been demonstrated through experiments that the proposed method achieves an accuracy of 97.93% in matching tasks across different series and 95.48% within the same series with a retrieval time of 0.3 seconds. The accuracy and matching speed are significantly improved compared to other algorithms, further validating the effectiveness of this approach.