Big Data Analysis Application in News Service Mode Based on Genetic Algorithm

Abstract

Due to the development and popularization of technologies such as the Internet, traditional forms of media have been greatly affected. Therefore, the way people receive news has also undergone certain changes. Due to the dramatic increase in the amount of information data and the variety of sources that are very rich, the complexity of the rational use of data information is also increasing. In order to improve the speed and accuracy of text classification, for the problems of classification efficiency and classification accuracy, this study adopts a large-scale text classification method based on genetic algorithm optimization. After designing the experimental analysis, the optimized genetic algorithm can be used to classify effectively, thus providing a new processing idea and method for the news application in the context of big data. First, we analyze and review excellent data work at home and abroad, emphatically analyze the impact of new news models on traditional news production, and propose that in the current news service model, big data can be combined to realize informatization applications. To predict information in the context of current data results, the application and sharing of data information can be realized by designing an open-source news database. The finished design of the media database can be used as the basis for the development of self-news and can take the lead in the news competition. This work analyzes and studies big data technology and genetic algorithms and introduces them into the field of news service model design, thus promoting the development of new news models.