Feiyi Tang, Ph.D., is a full-time faculty member at the School of Information Engineering, Guangzhou Vocational and Technical University. His teaching and research interests include knowledge graphs, link prediction, recommender systems, graph neural networks, community detection, natural language processing, and the application of artificial intelligence in education. He has overseas study and research experience, enterprise practice experience, and teaching experience in vocational higher education. His long-term research focuses on intelligent graph data analysis, knowledge representation learning, intelligent recommendation, and the application of AI technologies in education.

Dr. Tang received his bachelor’s degree in Information and Knowledge Management from RMIT University, his master’s degree in Information Systems from the University of Melbourne, and his Ph.D. in Information and Mathematical Sciences from Victoria University. During his doctoral studies, he conducted research on “Link Prediction and Its Application in Online Social Networks,” which laid a solid foundation for his research in complex networks, social network analysis, link prediction, graph representation learning, and intelligent recommendation.

Since joining Guangzhou Vocational and Technical University, Dr. Tang has been working on the front line of vocational education. He has taught courses such as Introduction to Artificial Intelligence, Python Programming, Python Course Design, Computer Networks, Graduation Project, Internship, and Comprehensive Design Project Courses. He emphasizes the integration of artificial intelligence, big data, knowledge graphs, and software engineering practices into classroom teaching, aiming to cultivate students’ programming ability, engineering practice ability, innovation ability, and professional competence. He holds a Higher Education Teacher Qualification Certificate, a Guangdong Vocational Education “Dual-Qualified” Teacher Certificate, and has passed the professional competence assessment of the Guangdong Institution of Engineers, reaching the level of Senior Engineer in Computer Technology and Applications.

Knowledge Graphs and Knowledge Representation Learning
Dr. Tang conducts research on knowledge graph entity semantic representation, temporal semantic modeling, knowledge-enhanced recommendation, knowledge graph construction, and related applications. He focuses on the application of knowledge graphs in recommender systems, educational resource organization, and intelligent services.
Graph Neural Networks and Graph Representation Learning
His research covers graph data modeling, graph embedding, link prediction, node representation learning, and community detection. He explores the integration of graph structural information, node attribute information, and deep learning models.
Recommender Systems and Personalized Services
He focuses on session-based recommendation, multi-interest recommendation, news recommendation, knowledge graph-enhanced recommendation, and deep reinforcement learning-based recommendation. His work aims to optimize user interest modeling, candidate-aware content modeling, and personalized recommendation algorithms.
Natural Language Processing and AI in Education
His research also involves named entity recognition, text classification, Prompt learning, large language model applications in teaching, course knowledge graphs, knowledge tracing, and intelligent exercise recommendation. He explores how AI technologies can support the reform and development of vocational education.

March 2014 – December 2017
Victoria University, Ph.D. in Information and Mathematical Sciences.

March 2012 – December 2013
University of Melbourne, Master’s degree in Information Systems.

February 2009 – November 2011
RMIT University, Bachelor’s degree in Information and Knowledge Management.

February 2022 – Present
Guangzhou Vocational and Technical University, School of Information Engineering, Full-time Faculty Member.

November 2019 – September 2021
Enterparagon (Melbourne), Office Manager / Information Systems Developer.

May 2018 – January 2022
Guangzhou E-Flight Information Technology Co., Ltd., Researcher.

December 2017 – September 2021
Raised International (Melbourne), Founder / Lecturer.

July 2015 – October 2017
Victoria University, Teaching Assistant.

April 2014 – April 2016
University of Melbourne, Research Assistant.

Dr. Tang has long been engaged in teaching courses related to software technology, artificial intelligence, and computer science. His teaching responsibilities include Introduction to Artificial Intelligence, Python Programming, Python Course Design, Computer Networks, Graduation Project, Internship, and Comprehensive Design Project Courses. In his teaching practice, he emphasizes the integration of theory and practice, project-based learning, task-driven learning, and real-world case studies, with the goal of improving students’ software development skills, AI application skills, and engineering practice abilities.

Since assuming his current position, Dr. Tang has continuously undertaken professional course teaching tasks and maintained a solid teaching workload. Based on the university’s teaching arrangements and his enterprise practice experience, he has completed teaching tasks across multiple semesters in courses such as Introduction to Artificial Intelligence, Python Programming, Computer Networks, Graduation Project, and Internship. He actively transforms research outcomes, enterprise project cases, and emerging industry technologies into teaching resources, promoting the alignment of classroom content with industrial trends and professional competency requirements.

In teaching reform, Dr. Tang focuses on the integration of artificial intelligence technologies with vocational education curricula. He actively participates in course resource development, textbook case development, practical project design, and student competition guidance. He also explores a talent cultivation model that combines classroom teaching, project practice, competition training, and professional ability development.

Dr. Tang has led the Guangzhou Science and Technology Plan Project titled “Research and Application of Temporal Semantic Representation Models for Knowledge Graph Entities.” The project belongs to the Basic Research Program, with the task number 2023A04J1728, and was implemented from April 2023 to March 2025. The project has successfully passed acceptance. It focuses on temporal semantic representation of knowledge graph entities, knowledge modeling, and related applications, closely aligning with Dr. Tang’s research foundation in knowledge graphs, graph representation learning, link prediction, and recommender systems.

In addition, Dr. Tang actively participates in research related to artificial intelligence, knowledge graphs, graph neural networks, recommender systems, and natural language processing. He emphasizes the integration of research problems with education, industrial applications, and the digital transformation of vocational education, promoting the application of research outcomes in curriculum development, student training, and social services.

In recent years, Dr. Tang has published multiple papers in the areas of graph neural networks, graph representation learning, community detection, intelligent recommendation, knowledge graphs, natural language processing, and AI in education. Selected publications include:

Chang Chao, Tang Feiyi*, Yang Peng, Zhang Jingui, Huang Jingxuan, Li Junxian, Li Zhenjun. Multi-view knowledge representation learning for personalized news recommendation. Scientific Reports, 2025. SCI Q2.
Tang Feiyi, Li Junxian, Liu Xi, Chang Chao, Teng Luyao. GATFELPA integrates graph attention networks and enhanced label propagation for robust community detection. Scientific Reports, 2025. SCI Q2.
Yuan Chengzhe, Tang Feiyi, Shan Chun, Shen Weiqiang, Lin Ronghua, Mao Chengjie, Li Junxian. Exploring Named Entity Recognition via MacBERT-BiGRU and Global Pointer with Self-Attention. Big Data and Cognitive Computing, 2024. SCI Q2.
Teng Luyao, Tang Feiyi, Chang Chao, Zheng Zefeng, Li Junxian. Gig: a knowledge-transferable-oriented framework for cross-domain recognition. Multimedia Systems, 2024. SCI Q2, CCF C, CAAI C.
Ronghua Lin*, Feiyi Tang*, Chengzhe Yuan, Hao Zhong, Weisheng Li*, Yong Tang. DeHier: Decoupled and Hierarchical Graph Neural Networks for Multi-Interest Session-based Recommendation. World Wide Web Journal, 2024. CCF B, SCI Q3.
Ronghua Lin, Feiyi Tang*, Chaobo He, Zhengyang Wu, Chengzhe Yuan, Yong Tang. DIRS-KG: a KG-enhanced interactive recommender system based on deep reinforcement learning. World Wide Web Journal, 2023. CCF B, SCI Q3.
Ying Li, Yong Tang, Junwei Cheng, Chaobo He, Feiyi Tang*. A Community Detection Method to Counter the Semantic Noise of Complex Networks. IEEE Systems, Man, and Cybernetics Magazine, 2023. SCI Q3.
Weisheng Li, Feiyi Tang*, Chao Chang, Hao Zhong, Ronghua Lin, Yong Tang. Efficient Graph Embedding Method for Link Prediction via Incorporating Graph Structure and Node Attributes. Web Information Systems Engineering – WISE 2023. CCF B, CAAI C.

Dr. Tang has participated in the application and authorization of several invention patents, covering course knowledge graphs, knowledge tracing, programming exercise recommendation, personalized recommendation, link prediction, and data storage system optimization. Major patents include:

Exercise Recommendation Method and Device Based on Course Knowledge Graph and Knowledge Tracing, invention patent application, 2024.
Programming Exercise Recommendation Method and System Based on Knowledge Tracing and Text Matching, granted invention patent, 2024.
Personalized Recommendation Method and System Integrating Implicit Feedback and User Social Status, granted invention patent, 2023.
Incremental Update Method, Device, Equipment, Medium, and Product for In-Memory Storage Systems, granted invention patent, 2023.
Link Prediction Method and System Based on Topological Structure and Attribute Information, invention patent application, 2023.
Teaching and Research Awards

Dr. Tang has actively participated in teaching reform, research innovation, and teaching ability competitions, and has received several awards:

First Prize, Guangdong Computer Society Education and Teaching Achievement Award, 2024, ranked first.
Second Prize, Guangdong Vocational College Skills Competition Teaching Ability Competition, Professional Course Group I, Higher Vocational Group, 2024. Project title: “Visual Intelligent Inspection of Power Batteries.”
Second Prize, Guangdong Vocational College Skills Competition Teaching Ability Competition, Higher Vocational Group, 2023. Project title: “Preliminary Study on Visual Recognition Technology and Applications.”
Second Prize, Guangdong Computer Society Science and Technology Award, 2023, ranked third.
First Prize, Guangdong Computer Society Science and Technology Award, 2024, contributor.
Student Guidance

Dr. Tang actively undertakes class advisor work and student development guidance. He serves as the class advisor for the 2022 Blockchain Technology Application program, providing guidance in class management, academic development, professional learning, and career planning.

In student competitions and innovation and entrepreneurship training, Dr. Tang has guided or participated in guiding students in competitions such as the “Challenge Cup” Guangdong College Student Entrepreneurship Plan Competition, the China International College Students’ Innovation Competition, and the Cross-Strait, Hong Kong and Macao College Student Computer Innovation Works Competition. His students have won awards including provincial silver prizes and a second prize in the national finals. He emphasizes the use of competition projects to improve students’ problem analysis ability, teamwork ability, technical implementation ability, and innovation practice ability.

Dr. Tang attaches great importance to the development of “dual-qualified” teaching ability and industrial practice competence. From September 2024 to September 2025, he participated in the Teacher Enterprise Practice Program and carried out a one-year enterprise practice at Guangdong Hengdian Information Technology Co., Ltd. as a software engineer. During the enterprise practice, he worked on Python programming, artificial intelligence, big data, enterprise product development, technical service processes, textbook case development, research paper and patent cultivation, and software copyright applications. Through this experience, he gained a deeper understanding of real enterprise project workflows and job competency requirements, and applied the experience to classroom teaching and program development.

He has also participated in training programs such as the DeepSeek Large Model Teaching Application Practice Faculty Training Program and the National Digital Talent Technical Enhancement Backbone Faculty Training Program. Through continuous professional development, he keeps updating his knowledge in artificial intelligence, large language models, digital technologies, and educational applications, providing support for curriculum reform, teaching resource development, and exploration of AI applications in education.

Overall, Dr. Tang has overseas study and research experience, vocational higher education teaching experience, enterprise practice experience, and a stable record of research achievements. His research focuses on knowledge graphs, graph neural networks, link prediction, intelligent recommendation, community detection, natural language processing, and AI in education. He is committed to integrating research outcomes, enterprise practice, and vocational education reform to support curriculum development, student training, and program construction.

In the future, Dr. Tang will continue to focus on the development needs of vocational education and carry out teaching and research in artificial intelligence, knowledge graphs, graph neural networks, intelligent recommendation, large language model applications in education, and the digital transformation of vocational education. He will further enhance his capabilities in curriculum development, research innovation, student guidance, and social service, contributing to the university’s program development, talent cultivation, and the growth of the regional digital industry.