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| Hierarchical Discipline Classification with Multi-feature Synergy and Its Application to Interdisciplinary Measurement |
| Zong Wei, Zhang Xin, Yang Xiaoxuan, Zhang Jianuo |
| School of Economics and Management, Xidian University, Xi’an 710126 |
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Abstract In this study, interdisciplinary measurements were conducted from the perspective of text content using hierarchical multi-label text classification methods to reveal the characteristics of interdisciplinarity more intuitively, thereby deepening and improving the interdisciplinary knowledge system. To address the problem of insufficient utilization of multidimensional features, such as paper text, the semantic and hierarchical structure of discipline labels, and the interactive relationship between paper text and discipline labels in the existing multi-label hierarchical classification of academic papers, a hierarchical multi-label discipline classification model based on multiple features, HMDCMF was constructed. The Centre for Research & Development Monitoring (ECOOM) discipline classification system was used to classify academic papers by discipline, and the interdisciplinarity of different disciplines was measured based on the discipline classification probability matrix. Compared with current mainstream hierarchical multi-label text classification models, the HMDCMF model performed the best. Interdisciplinarity was widespread across disciplines. Many disciplines are breaking through traditional development models, constantly integrating new fields and technologies and exploring new directions. By leveraging the HMDCMF model, the issues of insufficient semantic information in discipline labels and inadequate associations between paper text and discipline label features were alleviated, achieving interdisciplinary measurements from the perspective of paper content. At the theoretical level, this study expands the current methods of interdisciplinary measurement. In practice, it can promote the integration and collaborative innovation of knowledge among disciplines, providing a quantitative analysis basis for deepening the construction of an interdisciplinary knowledge system.
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Received: 20 August 2025
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