Topicality. Recently, more and more attention has been paid to the issues of machine learning in developed countries. On the one hand, this is due to the rapid growth of requirements for future specialists, and on the other - with the very rapid development of information technology and Internet communications. One of the main tasks of e-learning is the task of classification. The mathematical modeling system of decision trees is well adapted for the solution of the classification problem. However, as the number of input data increases, the issue of reducing the time of tree construction is becoming relevant. Using parallel computing systems and parallel programming technologies can produce positive results, but requires the development of new methods for constructing tree solutions. Results. The article reveals the main stages of the parallel tree construction method for solving the classification problem in e-learning. Unlike existing ones, the method allows to take into account the features of architecture and the organization of parallel processes in computing systems with shared and distributed memory. The method takes into account the possibility of evaluating performance indicators for constructing decision trees and parallel algorithms. Obtaining performance indicators for each iteration of the method helps to select the rational number of parallel processors in the computing system. This allows you to further reduce the time of building tree solutions. The simulation with the use of MPI parallel programming technology, the Python programming language for the architecture of the DM-MIMD system, confirms the reliability of the results. Here is an example of the organization of input data. Presented by Python is a program for building a decision tree. Conclusion. The developed visualization of the obtained estimates of performance indicators allows the user to select the necessary configuration of the computing system.