In the field of short-text intent recognition, convolutional neural networks (CNN) have garnered considerable attention due to their outstanding performance in extracting local information. Nevertheless, their limitations arise from the difficulty in capturing the global features of short-text corpora. To address this issue, this study combines the strengths of TextCNN and BiGRU-att to propose a dual-channel short-text intent recognition model, aiming to better recognize the intent of short texts by leveraging both local and global features, thereby compensating for the model’s inadequacies in capturing overall text features. The AB-CNN-BGRU-att model initially utilizes an ALBERT multi-layer bidirectional Transformer structure to vectorize the input text and subsequently feeds these vectors separately into TextCNN and BiGRU network models to extract local and global features, respectively. The fusion of these two types of features, followed by passing through fully connected layers and inputting into the Softmax function, yields the intent labels. The experimental results demonstrate that on the THUCNews_Title dataset, the proposed AB-CNN-BGRU-att algorithm achieves an accuracy (Acc) of 96.68% and an F1 score of 96.67%, exhibiting superior performance compared with other commonly used intent recognition models.