New Media Big Data Analysis and Forecast Based on Deep Learning

Abstract

In recent years, with the advent of the third wave of artificial intelligence, a large number of new technologies have been applied in social software and information services, such as big data collection and analysis, pattern recognition and user behavior prediction. The subsequent impact of mobile new media on a new generation of young people and the entire society has become one of the most promising and research issues. This paper obtains relevant data through online and offline surveys and authoritative organizations, uses Cronbach alpha coefficient to verify the validity of the survey results. The Spearman Rank correlation coefficient is calculated to analyze the relationship between the grade level of college students, the degree of influence on mobile new media and the duration of using mobile new media. Based on BP neural network and Elman neural network, the two models are combined by the optimal weighting method to predict the arrival rate of mobile new media information, and the conclusion based on quantitative analysis of mathematical model is given. The results show that the arrival rate of new media information on different platforms is on the rise in the future.