New Media Detection Technology Based on Big Data Network Intelligent Algorithm

This paper aims to address the current issues of difficult classification of false information and low detection accuracy. For the issue of detecting and categorizing false information, a new media detection technology framework on the basis of big data network intelligent algorithm is constructed. Through the learning model C-BiLSTM (Convolutional Bidirectional Long Short-Term Memory) and natural language processing (NLP) technology, the problems of difficult classification of false information and low detection accuracy are effectively solved. Through NLP technology, key features are extracted from large-scale new media data to identify different types of false information (such as misleading content, forged information, etc.), and the C-BiLSTM model framework is combined to precisely track the information propagation path and source. In the experimental report of this paper, the model built by C-BiLSTM can achieve an accuracy of 95% for data classification, and the model stability is good. The C-BiLSTM model is used to classify new media false information, which can analyze the results more effectively and accurately. Different information is classified and summarized to improve users’ Internet security and provide effective instructions for dealing with new media false information.