Abstract: With the development of We-mediaevery individual can publish and forward information on the internet at will.The information may have real recordsbut it may also be hearsay or even contents being intentionally tampered with.The data on the Internet has serious redundancy and weak credibility problemsthus resulting in low availability of existing network media data.Although the Bi-LSTM-CRF network can solve the problem of the accuracy of named entity recognition in datait cannot meet the requirement that the identified entity is credible.In this papera multi-parameter fusion credible confirmation algorithm based on multi-source weakly trusted data is proposedwhich is verified by identifying instances of person named entities.This paper uses distributed spiders to crawl Top N pages with the same mailbox address on multiple search engines.AfterwardsBi-LSTM-CRF algorithm trained by bilingual corpus is adopted to extract person named entities from each page.Finallythe person named entities corresponding to the mailbox are determined by multi-parameter entity fusion trusted confirmation algorithm.The experimental results show that the multi-parameter fusion credible confirmation algorithm can improve the accuracy of MRR (MRR) of the matching between the mailbox address and the real owner of the mailbox to 91.32%which is 23.08% higher than the traditional algorithm using only the term frequency model.The experimental data reasonably demonstrates that the multi-parameter fusion credible confirmation algorithm can obtain strong credibility entities from weakly trusted data and reduce the low-quality characteristics of massive datathus effectively enhancing the credibility of entity data sources.

Key words: Bi-LSTM-CRF, Edit distance, Multi-parameter fusion trusted confirmation algorithm, Weak trusted data