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[Purpose/Significance] Interdisciplinary research has emerged as a key driver of knowledge innovation, its essence being integration of knowledge from different disciplines. A deep and nuanced understanding of the research content of interdisciplinary knowledge integration is essential to foster innovation beyond traditional academic boundaries. Most of the research reviews on interdisciplinary research summarize theories, concepts and indicators from the macro level, while the content of interdisciplinary knowledge integration at the micro level is scattered, and lacks systematicity and logic. Therefore, this study attempts to classify and summarize the research methods, research content and research ideas involved in order to provide reference for scholars and researchers engaged in interdisciplinary knowledge integration. [Method/Process] Based on Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) database as the primary data sources, we reviewed the relevant literature on interdisciplinary knowledge integration. By clarifying the intrinsic meaning of interdisciplinary knowledge integration, we have systematically reviewed and summarized the existing research findings from three aspects. First, research on the degree of knowledge integration mainly involves measurement methods. Second, research on the content of knowledge integration is based on citations and keywords. Third, research on the process of knowledge integration involves its integration path and process, and the characteristics of the integration stage. On this basis, this paper also summarizes the limitations and challenges of existing research, and provides research perspectives for subsequent research on interdisciplinary knowledge integration. [Results/Conclusions] The existing results have extensively explored the definition, measurement method, content, and process of interdisciplinary knowledge integration, but there are problems such as limited applicability of knowledge integration measurement methods, insufficient semantic disclosure, and the lack of systematic research on the degree of knowledge integration. Future research in this area should pay attention to the following three aspects. First, the applicability of methods for measuring interdisciplinary knowledge integration should be improved in order to better adapt to the development of interdisciplinary knowledge integration. Second, there is also a need to strengthen the study of knowledge integration from the perspective of knowledge increment, examining how new knowledge is created and how it contributes to the advancement of the area. In addition, efforts should be made to explore new theories and methods, grasp the development law of interdisciplinary knowledge integration, and expand the application scope and application value of interdisciplinary knowledge integration.

[Purpose/Significance] Interdisciplinary research can creatively solve complex problems in natural environment and human society through knowledge integration and penetration. With the increase of interdisciplinary research results, the evaluation of interdisciplinarity becomes increasingly necessary. How to establish an effective method for interdisciplinarity measurement and achieve a comprehensive measurement of scientific research papers is an urgent problem to be solved. [Method/Process] Based on the above background, this study takes the data of scientific research papers as the analysis source, deconstructs the interdisciplinarity of scientific research papers from multiple dimensions, constructs the feature set of interdisciplinarity of scientific research papers, and on this basis proposes the method for measuring interdisciplinarity based on the adaptive method of machine learning, and conducts a comprehensive measurement of interdisciplinarity. This study has certain positive significance for researchers to understand the interdisciplinary papers in the field. The work process is as follows: First of all, the basic concepts of interdisciplinarity are sorted out and related concepts are discriminated, and the index of interdisciplinarity of different dimensions is analyzed. Based on the connotation and characteristics of interdisciplinary research, the characteristic index of interdisciplinarity of scientific research papers is extracted from three dimensions: subject attribute, knowledge network topology and knowledge integration text content. Secondly, an interdisciplinarity measurement method based on machine learning is constructed. By analyzing information gain and feature similarity of input indexes and data in feature sets, a feature selection calculation method based on adaptive feature selection is proposed, and the accuracy of feature classification is maximized by machine learning classifier. At the same time, the feature subset that can best express the interdisciplinary is selected based on the adaptive selection of the minimum number of features, and the selected adaptive feature set is used in the calculation of the interdisciplinary of the paper, and the results of the calculation of the original feature set are analyzed comprehensively. Finally, an empirical study was carried out in the field of plant nanobiotechnology to verify the effectiveness of the index system and adaptive feature selection listed above, identify and screen papers with high interdisciplinarity in the field, measure the interdisciplinarity of papers and identify key influencing factors based on the calculation of subject attributes, knowledge network topology and knowledge integration text content features. [Results/Conclusions] The main empirical results show that, among the subject attributes, the balance degree and the difference degree have a greater effect on the interdisciplinary evaluation. The overall effect of knowledge network topology structure features is satisfactory, the distribution breadth of knowledge integration text content features has a greater effect on interdisciplinary evaluation, and the calculation effect is further improved by fitness weighted summation of each feature. The results demonstrate that the adaptive feature selection proposed in this paper can effectively screen the interdisciplinary related feature indexes, improve the reliability of the results, and achieve a comprehensive and in-depth measurement of the interdisciplinary of scientific research papers. This measure method avoids the subjective defects that may occur in qualitative evaluation and the problems that different measure indicators may produce contradictory results. It provides a new idea and direction for interdisciplinary measurement.

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