Application of Random Forest Algorithm in New Media Network Operation Data Push

The application of big data technology in new media network operations has become a hot topic of current research. The traditional push algorithm would produce large data redundancy and data inconsistency, which would affect the push effect. Therefore, this paper proposed a new media network operation data push system based on random forest algorithm, and conducted research from three aspects: network operation data feature analysis, feature selection, and random forest algorithm model construction. Through the analysis of Sina Weibo data, the characteristic variables of Weibo network operation were determined, and a network operation model was established based on random forest algorithm. On this basis, the user groups in new media network operation were classified, and the user groups of different classifications were pushed to users, providing effective reference for new media network operation. It was found that most users in the operation of new media networks are active users, and the number of comments and retweets are higher than average; while some low-active users are low-comments and low-retweets, and the number of comments and retweets are lower than average.