Application Of Improved Combined Decision Tree Algorithm In The Discernment Of Proverbs

With the vigorous development of the Internet,the vast amount of information that people are exposed to each day is mixed.The rumors that they are spread arbitrarily may even cause social group events and cause serious consequences.How to accurately make rumors in big data environment has important social significance.Decision tree classification algorithm is an effective method to solve the problem of rumors discrimination.In the process of discrimination,not only the accuracy of rumors discrimination and work efficiency should be considered,but also the question of whether the discrimination method can be applied to the judgment of other platform rumors should be considered.However,the traditional rumors discriminating method uses the public opinion information of a specific social platform as the research object,which results in the method is not universal,and different methods are needed to discriminate rumors on different platforms.Therefore,a rumor discriminating method suitable for multi-platform is significant for improving the efficiency of discriminating the overall network rumor.The rumor discriminant model designed in this paper fully considers the requirements of rumors discriminating work in big data environment,combines text processing technology,decision tree algorithm,and big data technology,integrates these technologies from a global perspective,and makes corresponding improvements to the decision tree algorithm.The discriminating method is suitable for handling multi-platform rumor discrimination work.While improving the universality of the discriminating method,the discriminating work efficiency is improved.