| The rapid advancement of artificial intelligence (AI) is profoundly influencing the publishing processes of scientific journals. As the starting point of journal publishing, topic selection and planning has become a critical focus for intelligent transformation. While current approaches centered on conversational AI have improved efficiency, they also face limitations such as opaque generation processes, homogenized content, and difficulty in verifying results. To address these issues, this paper proposes a novel topic selection and planning model characterized by "editor-led, data-driven, and AI-assisted" approaches, and systematically elaborates on this model from three dimensions: data collection, analytical tools, and planning practices. In addition, the paper proposes corresponding strategies to address the practical challenges of applying AI to topic selection and planning. The study concludes that the value of AI lies in assisting rather than replacing editors' judgment. The key is to establish a human-AI collaboration mechanism with editors at the core, data as the driver, and AI as the tool, enabling data to inform decision-making and forming an iterative planning pathway, thereby providing theoretical and practical references for scientific journals to enhance their topic selection and planning capabilities. |
