A visual induced motion sickness (VIMS) estimation model based on attention mechanism was proposed to accurately assess the degree of VIMS experienced by users when interacting with virtual products. The model was constructed upon Transformer architecture, incorporating the self-attention mechanism within temporal and spatial sequences to capture the complex interactions between temporal and spatial features. By utilizing the optical flow information and user attention information, two sub-networks of motion flow and attention flow were designed to form a dual-flow network structure. The motion flow sub-network was responsible for capturing the motion features in the visual content, and the attention flow sub-network focused on extracting critical information, such as objects, textures, and other key elements within the user’s attention area. A late fusion strategy was employed to effectively combine the outputs of the dual-flow network. Experimental validation conducted on public video datasets demonstrated that the synergistic interaction between the attention flow sub-network and the Transformer architecture significantly enhanced the model accuracy. The VIMS model achieved optimal results in terms of the F1 score, accuracy and precision with values of 0.8468, 89.19% and 92.28%, respectively, representing a notable advancement over existing approaches.