<p>Large language models (LLMs) are central to AI systems and Excel in natural language processing tasks. They blur the line between human and machine-generated text and are widely used by professional writers across domains including news article generation. The challenge of detecting LLM-written articles introduces novel obstacles regarding misuse and the generation of fake content. In this work, we aim to recognize two kinds of LLM-written news where one type is entirely generated by LLMs and another is paraphrased based on existing news sources. We propose a neural network model that incorporates linguistic features and BERT contextual embedding features for LLM-written news article detection. In conjunction with the proposed model, we also produce a news article corpus based on the BBC dataset to generate and paraphrase news articles through multi-agent cooperation using ChatGPT. Our model obtains 96.57% accuracy and 96.44% F1_macro score, respectively, outperforming other existing models and indicating the capability of helping readers to identify LLM-written news articles. To assess the model’s robustness, we also construct another corpus based on the BBC dataset using a different language model, Claude, and demonstrate that our detection model achieves strong results. Furthermore, we apply our model to text generation detection in the medical domain, where it also delivers promising performance.</p>