Research on optimisation of electronic information engineering course structure and modular design based on graph neural network

Graph neural network, as a powerful data processing tool for graph structure, provides a new perspective for the optimization and modular design of electronic information engineering course structure. This paper integrates the graph convolutional neural network model into the electronic information course knowledge map and constructs a graph convolutional network recommendation algorithm based on the knowledge map. By designing an online learning platform to optimize the structure of the electronic information course, the modular teaching mode of the course is designed in this way. The recommendation model constructed in this paper converges to about 0.89 and 0.13 in precision and recall of training data and validation data after 40 rounds of iteration, and the recommendation performance of the model in this paper is better than the basic model. When the number of recommendations is 40, the model of this paper still shows better recommendation performance; its recommendation performance is best when the learning rate is 0.005, the embedding dimension is 64, and the aggregation method is bi-interaction. After the experiment, the average level of EE knowledge in the experimental class increased by 7.56 points compared to the control class, which showed a significant difference. And the student’s satisfaction with the teaching method of this paper is high. This paper provides key technical support for optimizing and modularizing the structure of electronic information engineering courses using graph neural networks.