## 1 Introduction  
The outbreak of coronavirus has made colleges and universities fully aware of the importance of information-based teaching [1; 2; 3]. A school's traditional theory combined with an experimental teaching mode is very resource-intensive and can no longer meet the learning needs of students. Only the integration of traditional teaching and information can ensure higher teaching quality [4; 5; 6]. As a professional course that analyzes the fundamental laws and application principles of various electromagnetic phenomena, electromagnetic field theory places high demands on students' understanding and cognition, and has always been a difficult point for students to learn. A large number of difficult-to-understand formulas greatly hinder students' understanding and the understanding of abstract objective concepts [7; 8; 9].  
With the gradual maturity of finite element technology, different forms of electromagnetic fields can be realized through corresponding finite element calculations. The electromagnetic field that was originally invisible and intangible can be expressed in the form of a field diagram. This kind of visual teaching that actively adopts the means of modern educational technology can cultivate students' interest in learning, enrich the teaching content, greatly improve teaching efficiency, and bring the teaching to a new level. However, teaching based on finite element theory is still a computationally expensive process [10; 11; 12].  
On the other hand, with the rapid development of computer technology, research in fields such as machine learning and neural networks has grown rapidly. This research has been gradually applied in the power industry towards problems such as load forecasting, power forecasting of power plants, power grid fault diagnosis and so on [13; 14; 15]. In terms of magnetic field calculation, [PERSON] [16] proposed a three-dimensional finite element neural network calculation model and established a magnetic field analysis model based on a finite element neural network. The network did not need to be trained, and iteratively corrected weights from the hidden layer to the output layer to obtain the magnetic scalar potential distribution. Using the Hopfield neural network algorithm, the experts [PERSON] et al. proposed an automatic integral equation method with which two-dimensional field calculations could be performed in nonlinear magnetic media. They investigated further, using a coupled three-node Hopfield neural network to model interacting magnetized subregions. Their study monitored the local magnetization curves tracked in electromagnetic devices to estimate the evaluation of local hysteresis losses [17; 18].  
Based on the above research on electromagnetic field teaching, a magnetic field visualization teaching method based on finite element theory and neural network is proposed in this paper. Basic data are provided through a certain number of finite element calculations, after which reliable models are built through artificial intelligence methods. On this basis, the model is quickly calculated. In this paper, a magnetic field was used as an example to conduct research based on the above ideas. Firstly, the finite element method was applied to calculate the magnetic field of the magnetic core inductance. Then the neural network method was used to learn the finite element data set and train the model. After training, the results of the magnetic field were predicted by changing the input parameters. By comparing the finite element calculation results with the neural network calculation results, we found that this method improved the computational efficiency and quickly and accurately predicted the coil magnetic field under the given conditions. On this basis, this method can also be extended and applied to other research on magnetic field teaching.