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This paper aims to objectively present the research dynamics of China in the field of information behavior and its development trends. Firstly, it incorporates China’s research in the field of information behavior into the global research network of information behavior, analyzing the changes in the status of Chinese scholars and their research institutions in the global research network from 1991 to 2022, the trends in publication volume and the cooperation relationships with other countries. Then, it conducts a detailed analysis of China’s research categories, groups, theoretical models and hot topics in different information contexts in the past five years (2018–2022).

The study retrieved research literature related to information behavior in China from 1991 to 2022 in the Web of Science database. It then utilized a national/institutional cooperation network map to analyze the changes in the status of Chinese scholars/institutions in the global research network during this period, publication volume trends and cooperation relationships with other countries. Furthermore, it employed keyword co-occurrence network maps to analyze the key categories, groups, theories and models of China’s research in different information contexts in the past five years. Based on this, it used keyword clustering network maps to analyze the hot topics of China’s research in different information contexts in the past five years.

(1) China’s research in the field of information behavior started relatively late, but the volume of publications has grown rapidly since 2004, currently ranking second globally in cumulative publication quantity. However, the influence of the literature published by China is limited, and there is a lack of research institutions with global influence. (2) In the last five years, China has conducted extensive research in various information contexts. Among these, most research was conducted in work contexts, followed by healthcare contexts, especially studies related to epidemics. (3) Current research on information behavior in China is characterized by expanded and refined research groups, diversified research categories, continuous expansion and enrichment of research contexts, increased interdisciplinary nature of research and continuous innovation in research methods and theoretical models.

This study, utilizing a scientific knowledge map, elucidates China’s position in global information behavior research, with a specific emphasis on analyzing China’s research hot topics and trends in this field over the past five years. It aims to provide valuable resources for scholars interested in understanding the status of information behavior research in China and to offer some guidance for scholars currently or intending to engage in information behavior research.

The purpose of this paper is to develop a transform method and a deep learning model to identify the inner surface shape based on the measurement temperature at the outer boundary of the pipe.

The training process is assisted by the finite element method (FEM) simulation which solves the direct problem for the data preparation. To avoid re-meshing the domain when the inner surface shape varies, a new transform method is proposed to transform the shape identification problem into the effective thermal conductivity identification problem. The deep learning model is established to set up the relationship between the measurement temperature and the effective thermal conductivity. Then the unknown geometry shape is acquired by the mapping between the inner shape and the effective thermal conductivity through the inverse transform method.

The new method is successfully applied to identify the internal boundary of a pipe with eccentric circle, ellipse and nephroid inner geometries. The results show that as the measurement points increased and the measurement error decreased, the results became more accurate. The position of the measurement point and mesh density of the FEM model have less effect on the results.

The deep learning model and the transform method are developed to identify the pipe inner surface shape. There is no need to re-mesh the domain during the computation progress. The results show that the proposed method is a fast and an accurate tool for identifying the pipe inner surface.