With the advancement of scientific research and the rapid growth of the internet, academic users increasingly face challenges in obtaining accurate information about peer research. As a key component of big data analytics, user profiling has emerged as a critical focus in the scientific research community. While graph neural networks (GNNs) perform well in various graph learning tasks, their scalability to large graphs becomes problematic as the number of nodes increases due to computational complexity. To address this issue, this study proposes a novel academic user profiling model based on graph neural networks tailored to the unique characteristics of scientific research networks. The main contributions of this work are as follows: (1) We propose a simplified transformer architecture that reduces model complexity to a linear relationship with the number of nodes. (2) By integrating the simplified transformer with GNNs, neighborhood information is aggregated while maintaining global attention. The experimental results demonstrate that the proposed model delivers exceptional performance in terms of both accuracy and efficiency.