Research Article
Mining Recessive Teaching Resources of University Information Based on Machine Learning
@INPROCEEDINGS{10.1007/978-3-030-82565-2_15, author={Zheng Jingya and Jichao Yan}, title={Mining Recessive Teaching Resources of University Information Based on Machine Learning}, proceedings={Multimedia Technology and Enhanced Learning. Third EAI International Conference, ICMTEL 2021, Virtual Event, April 8--9, 2021, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2021}, month={7}, keywords={Machine learning Informatization teaching Recessive resources}, doi={10.1007/978-3-030-82565-2_15} }- Zheng Jingya
Jichao Yan
Year: 2021
Mining Recessive Teaching Resources of University Information Based on Machine Learning
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-82565-2_15
Abstract
The accuracy of mining implicit teaching resources in traditional universities is low, so a method of mining implicit teaching resources in universities based on machine learning is designed. Firstly, it designs the process of data mining, define the problem, collect and preprocess the data, execute the mining algorithm and then explain and evaluate it. The classification method of data mining is optimized. In this paper, the classification technology is neural network, and the artificial neural network unit is built by biological neuron structure, and the classification is completed by biological transfer and activation function. Finally, the machine learning algorithm is improved, and the ight is updated by introducing momentum scalar factor. In the contrast experiment, it chooses the data set and train the parameters, design the process of data mining, and count the relevant parameters of the data set. The experiment results show that the accuracy of the designed method is 4.03% higher than that of the traditional method.
