Research on fusion model based on deep learning for text content security enhancement - Shaomin WANG; Zheng WANG; Hua REN (Dianxin kexue)
The rapid expansion of information content on the internet and the mobile internet has resulted in violations of laws and regulations and bad information,which affects the content security of the internet space.Traditional text content security recognition methods based on matching of sensitive words ignore context semantics,resulting in high false positive rate and low accuracy.Based on the analysis of traditional text content security recognition methods,a fusion recognition model using deep learning and a model fusion algorithm process were proposed.Text content security recognition system based on the fusion recognition model using deep learning and experimental verification was introducted deeply.Results show that the proposed model can effectively solve the problem of high false positive rate caused by the lack of semantic understanding of traditional recognition methods,and improve the accuracy of the bad information detection.