Research on Intelligent Mining Methods of Multimedia Teaching Resources in Colleges and Universities in the Age of Big Data

Abstract

To solve the problem of difficult access to college multimedia resources in the era of big data, the present research proposes an intelligent mining method for college multimedia teaching resources. Collect multimedia teaching resource data from the Internet by using focused crawlers with the advantages of subject crawling and URL sorting; Use the methods of removing stop words, word segmentation and word frequency statistics to process the crawled data; Extract features from processed data; The features extracted by clustering analysis are classified. The number of categories is selected as the number of BP neural networks for combination, and the momentum method and learning rate adaptive adjustment strategy are introduced to improve the combined BP neural network. The extracted features are input into the improved combined BP neural network, and the intelligent mining results of university multimedia resources are output. The experimental results indicate that the focused crawler method can efficiently collect multimedia education resources in colleges and universities, and data preprocessing can efficiently reduce data redundancy. The double-feature extraction method can significantly enhance the recall and accuracy of data mining. It can realize the classified mining of multimedia teaching resources in academic centers and display the classified mining results of multimedia teaching resources in various disciplines.