The paper investigates an approach to sociological information processing based on the use of intelligent data analysis methods applied to the task of processing the results of a questionnaire survey. The advantages of intelligent analysis of sociological data in comparison with traditional statistical processing are discussed, as well as the implementation features and applicability limits of various intelligent data analysis methods in solving problems of association, clustering and classification. Structure and features of representation of respondents’ survey data are considered, the appropriateness is substantiated and the advantages of their processing based on the combination of various methods of intelligent analysis within an ensemble of models are discussed. A structure of an ensemble of models is proposed based on the combination and joint use of association rules, clustering algorithms and decision trees, which makes it possible to jointly process numerical and categorical data contained in the respondent’s answers to the questionnaire and also to interpret the results of data clustering. The paper describes the results of using the constructed ensemble of models for processing and analyzing the data of a sociological survey conducted as part of the annual project for monitoring the drug abuse situation in the Bryansk region in 2013 – 2018. The use of an ensemble of intelligent data analysis models for processing the results of a sociological survey not only makes it possible to detect patterns in them that cannot be otherwise detected by traditional methods of statistical processing, but also contributes to an increase in the reliability, completeness and coherence of the analysis results, due to which the analyst creates a holistic systemic picture of the studied social phenomenon or process.