One of the main conditions for ensuring information security is to prevent the spread of false and intentionally distorted information. Filtering the content of Internet information resources can serve as a solution to this problem. Recently, an approach using methods and mathematical models of artificial intelligence has been increasingly considered for the analysis and classification of disseminated data. The use of neural networks allows you to automate the process of processing a large array of information and connect a person only at the decision-making stage. The paper focuses on the learning process of a neural network. Various learning algorithms are considered: stochastic gradient descent, Adagrad, RMSProp, Adam, Adama and Nadam. The results of the implementation of text subject recognition using a recurrent neural network of the LSTM model are presented. The results of computational experiments are presented, an analysis is carried out and conclusions are drawn.

Keywords: information security, text analysis, artificial intelligence method, artificial neural network, recurrent LSTM network