The study of the architectural heritage of the Chinese diaspora has an important role and significance in China’s historical and cultural background in the preservation of cultural data, the restoration of images, and in the analysis of human social and ideological conditions. The images from the architectural heritage of the Chinese diaspora usually include frescos, decorative patterns, chandelier base patterns, various architectural styles and other major types of architecture. Images of the architectural heritage of the Chinese diaspora in Jiangmen City, Guangdong Province, China are the research object of this study. A total of 5073 images of diaspora Chinese buildings in 64 villages and 16 towns were collected. In view of the fact that different types of image vary greatly in features while there are only small differences among the features of the same type of image, this study uses the depth learning method to design the Convolutional Neural Network Attention Retrieval Framework (CNNAR Framework). This approach can be divided into two stages. In the first stage, the transfer learning method is used to classify the image in question by transferring the trained parameters of the Paris500K datasets image source network to the target network for training, and thus the classified image is obtained. The advantage of this method is that it narrows the retrieval range of the target image. In the second stage, the fusion attention mechanism is used to extract the features of the images that have been classified, and the distance between similar images of the same type is reduced by loss of contrast. When we retrieve images, we can use the features extracted in the second stage to measure the similarities among them and return the retrieval results. The results show that the classification accuracy of the proposed method reaches 98.3% in the heritage image datasets of the JMI Chinese diaspora architectures. The mean Average Precision (mAP) of the proposed algorithm can reach 76.6%, which is better than several mainstream model algorithms. At the same time, the image results retrieved by the algorithm in this paper are very similar to those of the query image. In addition, the CNNAR retrieval framework proposed in this paper achieves accuracies of 71.8% and 72.5% on the public data sets Paris500K and Corel5K, respectively, which can be greatly generalized and can, therefore, also be effectively applied to other topics datasets. The JMI architectural heritage image database constructed in this study, which is rich in cultural connotations of diaspora Chinese homeland life, can provide strong and reliable data support for the follow-up study of the zeitgeist of the culture reflected in architecture and the integration of Chinese and Western aesthetics. At the same time, through the rapid identification, classification, and retrieval of precious architectural images stored in the database, similar target images can be retrieved reasonably and accurately; then, accurate techniques can be provided to restore old and damaged products of an architectural heritage.

Cancer is one of the most difficult medical problems in today’s world. There are many factors that induce cancer in humans, and obesity has become an important factor in inducing cancer. This study systematically and quantitatively describes the development trend, current situation and research hotspot of the relationship between cancer and obesity by using document statistics and knowledge graph visualization technology. Through the visualization technology analysis of knowledge graph in this study, the research hotspot and knowledge base source of the relationship between cancer and obesity in the last 20 years have been ascertained. Obesity-related factors, such as immunity, insulin, adiponectin, adipocytokines, nonalcoholic fatty liver and inflammatory reaction, may affect the occurrence of obesity and increase the risk of cancer. Obesity-related cancers include respiratory cancer, colorectal cancer, hepatocellular cancer, prostate cancer, gastric cancer, etc. Our research provides direction and basis for future research in this field, as well as technical and knowledge basis support for experts and researchers in related medical fields.

The unique characteristics of frescoes on overseas Chinese buildings can attest to the integration and historical background of Chinese and Western cultures. Reasonable analysis and preservation of overseas Chinese frescoes can provide sustainable development for culture and history. This research adopts the image analysis technology based on artificial intelligence, and proposes a ResNet-34 model and method integrating transfer learning. This deep learning model can identify and classify the source of the frescoes of the emigrants, and can effectively deal with the problems such as the small number of fresco images on the emigrants' buildings, poor quality, difficulty in feature extraction, and similar pattern text and style. The experimental results show that the training process of the model proposed in this article is stable. On the constructed Jiangmen and Haikou fresco JHD datasets, the final accuracy is 98.41%, and the recall rate is 98.53%. The above evaluation indicators are superior to classic models such as AlexNet, GoogLeNet, and VGGNet. It can be seen that the model in this article has strong generalization ability and is not prone to overfitting. It can effectively identify and classify the cultural connotations and regions of frescoes.