The human visual system (HVS) is capable of responding in real-time to complex visual environments. During the process of freely observing visual scenes, predicting eye movements and visual fixations is a task known as scanpath prediction, which aims to simulate the HVS. In this paper, we propose a visual transformer-based model to study the attentional processes of the human visual system in analyzing visual scenes, thereby achieving scanpath prediction. This technology has important applications in human-computer interaction, virtual reality, augmented reality, and other fields. We have significantly simplified the workflow of scanpath prediction and the overall model architecture, achieving performance superior to existing methods.