The modern job scheduling system in supercomputer platforms is based on the estimates of the request for computing resources provided by users (often based on subjective considerations). However, it has been found that such estimates can be significantly inaccurate. In this regard, a practically important task arises: building a behavior model of user tasks executed in a supercomputer, identifying and evaluating critical deviations from the predicted behavior profile (based on an assessment of user confidence). Methods of nonlinear dynamics and topological data analysis are used to solve this problem. The article presents the results of experimental studies for various data sets obtained at the “Polytechnic Supercomputer Center” of Peter the Great St. Petersburg Polytechnic University. The Betti curves of the supercomputer user profile are calculated. The results of the evaluation of the comparison of several user profiles with the reference profile are presented. A desirability scale and numerical intervals for the proposed classes are proposed.