Research on Unstructured Mega Data Analysis Algorithm of Communication Network Based on Feature Fusion

In communication network mega data, unstructured data is characterized by large scale, diversity and timeliness. Traditional unstructured processing methods have been difficult to meet the data processing needs. Complex data sets and large data orders in modern mega data require professional analysis tools to realize analysis. Information fusion is a multi-source information processing technology, which can optimize and synthesize redundant information from multiple sensors in space and time, and obtain more accurate and complete values than single information source, and obtain the consistent description of the measured object. In order to effectively solve the problem of unstructured data model of communication network mega data, this paper proposes an algorithm for unstructured data analysis of communication network based on feature fusion, and analyzes the key problems in the process of unstructured data feature modeling, such as the storage of original data and feature data, the selection of feature space, information query and data visualization.