Analysis in big data of satellite communication network based on machine learning algorithms

With the development of satellite communication network technology, the amount of data generated every day and the rate of data growth are amazing. These different types of data (not necessarily structured) contain rich information. Based on this, a satellite for machine learning is proposed. First, a correlation analysis model is established, and a machine learning method is applied to the data prediction analysis of the satellite communication network. The basic idea and application process of the AdaBoost-BP algorithm are introduced in detail. Second, combined with the data features after dimensionality reduction processing, an efficient big data analysis model is trained. Third, in order to quickly locate the abnormal behavior of users under large-scale data, an algorithm for quantifying abnormal behaviors of users based on satellite communication network log information and quickly detecting and analyzing big data is proposed. Finally, experimental verification is performed. The experimental results verify that the method can improve the big data analysis capability of satellite communication networks.