Research on the Construction Method of Curriculum Teaching Knowledge Graph Based on Bi-LSTM and CNN Algorithm.

The aim of the paper is to explore a method of constructing a curriculum teaching knowledge graph by combining Bi-LSTM and convolutional neural network (CNN) algorithm. The field of education is constantly seeking innovation to improve teaching results and student learning experience. Knowledge graph, as an advanced technology of structured representation of knowledge, is expected to provide effective support for teaching management and personalized learning. First, this paper introduces the background and significance of the curriculum teaching knowledge graph. By establishing knowledge maps, we can more clearly present the knowledge system and correlation in the curriculum, which helps teachers to design more targeted teaching content and provide personalized learning paths for students. However, traditional knowledge graph construction methods are often faced with problems such as incomplete information capture and inaccurate semantic association, so it is necessary to introduce advanced deep learning algorithms to improve the quality of knowledge graph. Secondly, this paper elaborates on the construction method of fusion Bi-LSTM and CNN algorithm. Bi-LSTM, as a recurrent neural network capable of capturing sequence information, can better model the evolution process of knowledge in the course. As a CNN is good at extracting local features, CNN can effectively capture the spatial structure information in the knowledge graph. By integrating two, we can improve the expression ability and reasoning accuracy of knowledge graph. Further, the experimental results show that the fusion Bi-LSTM and the CNN algorithm have significantly improved the accuracy of information capture and inference compared with the traditional method. In summary, this paper proposes an innovative construction method of curriculum teaching knowledge graph by integrating Bi-LSTM and CNN algorithm, which provides new ideas and solutions for informatization and personalized teaching in the field of education. In the future, the applicability of this method in different disciplines and teaching scenarios can be further discussed, and more advanced technologies can be combined to continuously improve and expand the research. [ABSTRACT FROM AUTHOR]