This paper proposes a model of neural tree architecture with probabilistic neurons. These trees are used for classification of a large amount of computer grid resources to classes. The first tree is used for classification of hardware part of dataset. The second tree classifies patterns of software identifiers. Trees are implemented to successfully separate inputs into nine classes of resources. We propose Particle Swarm Optimization model for tasks scheduling in computer grid. We compared time of creation of schedule and time of makespan in six series of experiments without and with using neural trees. In experiments with using neural tree we gained the subset of suitable computational resources. The aim is effective mapping of a large batch of tasks into particular resources. On the base of experiments we can say that improvements have been made even for middle and small batch of tasks.