With the rapid advancement of educational technology, the complexity of attention allocation among university students in information-rich learning environments has significantly increased. Effective attention allocation is crucial for learning outcomes; however, traditional research methods face limitations in capturing and analyzing dynamically changing cognitive processes. This study aims to utilize neural network technology, particularly the model based on Time-series generative adversarial network (TimeGAN), to identify and analyze the patterns of attention allocation in university students’ cognitive processes. The TimeGAN model, selected for its proficiency in handling time-series data, is employed to reveal the dynamic shifts of attention across various tasks and states. The second part introduces a novel framework that combines a Customized Gate Control (CGC) model with a progressive hierarchical extraction model, aiming to more accurately simulate the management of attention switching in a multitasking environment. These approaches enhance the model’s adaptability to individual differences, offering support for the formulation of personalized learning strategies. Not only does this research expand the application of computational models in the field of educational psychology, but it also provides new theoretical foundations and practical tools for optimizing teaching designs and promoting cognitive development among students.