University Media Content Detection and Classification Based on Information Fusion Algorithm

Abstract

In order to further solve the problems in promoting the classification of media content in colleges and universities, the effective analysis and understanding of multimedia data content can be better realized based on the characteristics of multimedia data in colleges and universities, combining with the characteristics of rich information, large differences in performance, and large amount of large-scale data. This essay mainly introduces the technology of university media content detection and classification based on information fusion algorithm and focuses on the application of university multimedia content detection, analysis, and understanding, to explore the image discrimination auxiliary attribute feature learning and content association prediction and classification. A benchmark model for media content detection and classification is constructed. Through the model test, it is found that the F1 value of the model is more than 70%, the check rate is more than 80%, and the recall rate is more than 50%. On this basis, a content detection system based on campus network is constructed.