We employ a custom-designed convolutional-glial network architecture for the classification of large text corpora. The problem selected to demonstrate the high effectiveness of the proposed technique, was defined in collaboration with Poland’s largest railway operator. The task involves categorizing incoming reports (submitted by passengers, station staff, and railway line personnel) into appropriate departments based on their textual content. The input data consists of short texts containing complaints, inquiries, and remarks, which were transformed into vector representations using the BERT language model. A hybrid convolutional architecture was then applied, augmented with a glial-type control module that dynamically regulates the activation levels of CNN filters. This glial control layer, inspired by biological mechanisms, was trained alternately with the convolutional component, enabling better adaptation of information flow within the network. Experimental results confirm that this approach outperforms classic architectures in terms of classification accuracy, while also offering greater flexibility and development potential. The proposed solution can be effectively applied in customer service support systems and automated text analysis for public transportation.