https://jjcit.org/paper/242 10.5455/jjcit.71-1720086134 Imad Jamaleddyn,Rachid El Ayachi,Mohamed Biniz Convolutional neural networks (CNNs),Long Short-Term Memory (LSTM),Gated Recurrent Units (GRUs),Word embedding,Arabic-text classification 4 3630 1183 4-Jul.-2024 11-Aug.-2024 and 26-Aug.-2024 31-Aug.-2024 In the era of digital journalism, the classification of Arabic news presents a significant challenge due to the complex nature of the language and the vast diversity of content. This study introduces a novel multi-channel deep-learning model, CLGNet, designed to enhance the accuracy of Arabic-news categorization. By integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), the proposed model effectively processes and classifies Arabic-text data. Extensive experiments were conducted on multiple datasets, including CNN, BBC and OSAC, where the model achieved outstanding accuracy and robustness, outperforming existing methods. The findings underscore the effectiveness of our hybrid model in addressing the challenges of Arabic-text classification and its potential applications in automated news categorization systems.