Research and Application of Author Influence Recognition Algorithm for Open Review in the Era of Artificial Intelligence

With the rise of artificial intelligence and social media, Open Review and discussion have become emerging academic communication methods, making identifying influential authors in this way a new research direction. This article designs and proposes an author influence recognition algorithm based on open review, which analyzes the text of open review content on social media and forms multiple different label clusters to represent the author's different characteristics, thereby achieving multi angle recognition of author influence. Finally, this article conducted empirical research and application using the F1000 open review platform, and the results showed that the proposed algorithm can assign multiple label scores to each author, achieving multi angle recognition of author influence, making author influence more comprehensive and three-dimensional. Through relevant analysis with traditional identification methods, the author believes that the open review recognition algorithm can play a certain complementary role in identifying author influence. Therefore, it is necessary to incorporate the open review algorithm into the existing author influence evaluation and recognition system, so as to more comprehensively and vividly reflect the author's influence and personal characteristics, better provide and recommend truly high-quality and excellent authors and articles to the academic community, and promote the healthy dissemination of science.