Design of Teacher Portrait System Based on Knowledge Graph

This paper takes the junior high school education administration system, human resource management system, student system and official website as the research object. Then the knowledge graph is used to study the multi-attribute teacher image. The system has intuitive material integration function,...

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Bibliographic Details
Published in:2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA) pp. 693 - 697
Main Author: Zhao, Qiaoyue
Format: Conference Proceeding
Language:English
Published: IEEE 28.06.2024
Subjects:
convolutional network recommendation algorithm
Data mining
Data preprocessing
Education
Feature extraction
Human resource management
Knowledge engineering
Knowledge graph
Knowledge graphs
multi-feature
Optimization
Software development management
teacher teaching portrait
Training
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Summary:This paper takes the junior high school education administration system, human resource management system, student system and official website as the research object. Then the knowledge graph is used to study the multi-attribute teacher image. The system has intuitive material integration function, powerful interactive creation function, rich variable, function and program control function. The process line is used to connect each icon, which greatly improves the speed and quality of multimedia software development. The process of building the whole model is given in this paper. The image acquisition and analysis are completed. This paper extracts and generates feature entity and association information to generate portrait model. Finally, the design and implementation of the whole experiment are completed based on knowledge graph and Neo4j development environment. The joint recommendation method of knowledge graph based on multi-level structure is studied. The feature transformation matrix is removed during node updating in each level convolutional network, and its own connections are selectively maintained or removed. Through the real teaching data set, the analysis and application of teachers' teaching portraits are completed.
DOI:10.1109/ICIPCA61593.2024.10708989