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[Purpose/Significance] At the moment of library transformation, using new information technology to solve the difficulties in traditional business and optimizing the reader experience is one of the research contents of smart libraries. We use the cloud edge system technology to design and implement the Shanghai Library mobile phone book lending system, which turns readers' mobile phones into dispersed self-help book lending devices, which is a successful attempt to grafting new technology into traditional library services to solve the difficulties in traditional services and optimize readers' experience. [Method/Process] In this paper, the technology of edge collaboration is applied to the library self-service loan business. First, starting from the problems faced by RFID self-service loan and return equipment in the public library, the prototype system is determined to change readers' mobile phones into self-service book borrowing equipment for readers, so readers do not need to use fixed loan and return machine, or queue up at rush hour. Centering on reader experience, data security and replication and promotion of new services are regarded as the design objectives of the system, to explore the integration of cloud-edge collaborative technology into the system to meet the design objectives (the characteristics of the system users, system performance and the scalability and stability of the system). According to the preset performance indicators, the paper provides detailed analysis and technology selection of the system, with an introduction of what we considered and why we chose the application of the system. Finally we achieve the purpose of reducing cost and increasing efficiency and improving readers' experience. [Results/Conclusions] Out one month after the system went online, about 12,640 books were lent out by mobile mobile phone book lending system, accounting for 10% of the total circulation. Most of them were young readers (ranging from 18 to 50 years old), which showed that the system met the design expectation and reduced the demand for self-service book lending machines in libraries to a certain extent. Through the official launch of the mobile phone borrowing system, the feasibility of the application of cloud edge collaboration technology in the library is verified. Cloud edge collaboration can meet the application scenarios of multiple branch libraries, and has better scalability and stability in the promotion and replication of services. It is better than the traditional system architecture. At the same time, it really reduces the purchase demand of the East Library of Shanghai Library for the self-service borrowing machine.

This paper constructs a morphological extraction model of Ganan Hakka architectural elements based on feature fusion using ResNet as the backbone network. Then, the extraction method of Ganan Hakka architectural colors based on the combination of multivariate fuzzy C-mean clustering algorithm and the K-mean++ algorithm is designed. The improved recommendation algorithm intelligently matches the extracted color and morphological elements and applies them to modern architectural interior design. The results show that the average time of interior design intelligent recommendation is 3.8s, the overall accuracy is above 0.94, and the degree of interior design intelligent matching is in the range of [0.78,0.99]. Based on this study, Ganan Hakka’s architectural elements can be well combined with modern architectural interiors to promote traditional culture.

[Purpose/Significance] As important historical data, images in modern literature are increasingly valued by humanities researchers. The deep annotation of large-scale image resources has also become an important part of the construction of image data infrastructure. It is a new direction of image study to analyze the content of massive images by using technologies such as deep learning. The purpose of this paper is to address the challenges of automatic classification of large-scale modern document images, improve accuracy and efficiency in practical application through the empirical research of transfer learning based on a simplified EfficientNet network specifically optimized for modern document image classification. [Method/Process] This paper adopts the selective images of "Illustrated Century - Modern Chinese Literature Library" from Shanghai Library, which is a deep exploration of the image content in modern Chinese literature by the "National Newspaper Index". The research method is to improve the diversity of sample images by serial stacking of those selective 7,645 modern literature image data sets through imaging enhancement technologies such as cutting, white balance, tone separation, and affine transformation, based on the characteristics analysis of modern literature images. Then we conducted transfer learning by fine-tuning simplified EfficientNet depth convolution neural network model through the study of depth learning algorithms. Finally, an optimized model that performs well in modern literature image classification was identified. Our simplified model achieved an average classification Top1 accuracy of 90.97%, an average F1 value of 91.00%, which validated its simplification, efficient, and good generalization ability for modern literature image classification application. During the experiments, it was also found that some images had high similarity and phototropism in morphology, which led to not good-enough classification results. However, this does provide valuable insights for further optimization and simplification of EfficientNet network model. The performance comparison test results with ResNet50-vd network also fully demonstrate that the simplified EfficientNet network can more economically and efficiently support incremental iterative training of subsequent models for achieving high-precision artificial intelligence classification of modern literature image databases. [Results/Conclusions] The experimental results indicate that the model effectively improves the efficiency and accuracy of image classification, and thus it has certain application promotion value for solving the automatic classification challenges of large-scale images in modern literature. In the future, we will continue to explore its application in the extraction of digital image semantic information. Through digital image pre-processing and extraction of digital image content and characteristics, we will provide technical enablers for automatic extraction of semantic information, so as to truly reduce the workload of manual intervention and achieve semantic description of millions of image data.