The purpose of this article is to search and extract the necessary content, identifying curriculum topics. Classification and clustering of text documents are challenging artificial intelligence tasks. Therefore, an important objective of this study is to propose and implement a tool for analyzing textual information.
The study used Data Mining methods to analyze text data and generate educational content. The work used methods for classifying text information, namely, support vector machines (SVM), Naive Bayes classifier, decision tree, K-nearest neighbor (kNN) classifier.
These methods were used in developing the curriculum for the specialty “Cybersecurity” for the Faculty of Information and Telecommunication Technologies. About 48 curricula in this specialty were analyzed, topics and sections in disciplines were identified, and the content of the academic program was improved. It is expected that the results obtained can be used by specialists, managers and teachers to improve educational activities.

Currently, modern social technologies are used by hundreds of millions of users, are available free of charge, attractive and interesting. The article discusses the possibility of the use of social networks to improve e-learning institution of higher education. Considering the large amount of information disseminated by university students on the social network, it is proposed to use methods of data clustering - k-means (k-means) in the article, to personalize the content of educational materials. The results of the research can be used by teachers and instructors of higher education institutions to improve the content of the e-course and personalize e-learning.