Text classification, a fundamental technique in information retrieval, categorizes textual data to streamline search operations, significantly enhancing efficiency. This research introduces CNN, a novel hybrid deep learning model that advances text classification by synergistically combining the strengths of transformer models, CNNs, RNNs, and LSTMs. The model stands out with its innovative design, inputting ‘input ids’ and ‘attention mask’ into a pretrained transformer, followed by a series of operations leading to robust feature extraction. CNN outperforms baseline models, demonstrating superior accuracy in text classification. Extensive experimentation on both Chinese and English datasets substantiates this, with the model achieving the highest accuracies of 94.54% and 91.89%, respectively. This paper underscores the effectiveness of the proposed model in enhancing text classification, paving the way for more efficient information retrieval.