  Interdisciplinary collaboration has become a driving force for scientific
breakthroughs, and evaluating scholars' performance in interdisciplinary
researches is essential for promoting such collaborations. However, traditional
scholar evaluation methods based solely on individual achievements do not
consider interdisciplinary cooperation, creating a challenge for
interdisciplinary scholar evaluation and recommendation. To address this issue,
we propose a scholar embedding model that quantifies and represents scholars
based on global semantic information and social influence, enabling real-time
tracking of scholars' research trends. Our model incorporates semantic
information and social influence for interdisciplinary scholar evaluation,
laying the foundation for future interdisciplinary collaboration discovery and
recommendation projects. We demonstrate the effectiveness of our model on a
sample of scholars from the Beijing University of Posts and Telecommunications.
