Steady-state visual-evoked potential (SSVEP)-based brain–computer interfaces (BCIs) are prominent in the information interaction field due to their noninvasive nature. Fixed-window-based classification for labeled data fails to capture the dynamics of the whole process in target selection and recognition, leading to resource inefficiencies. In response, we propose an attention-focused triggering (AFT) strategy for dynamic classification (DC), drawing inspiration from the vision-based attention system and dynamic window detection. Specifically, the attention and visual regions are extracted from the entire brain, with the attention region guiding vision to focus on stimuli, facilitating the detection in the visual region. This strategy quantifies attention indicators in the attention region and sets the data onset for target selection by judging concentration levels. Subsequently, the visual region data are dynamically truncated based on the prediction coefficient to enhance decision-making efficiency. As a result, the proposed method can minimize the necessary data length without compromising accuracy. In an experiment involving a nine-target BCI with 15 healthy subjects, the results demonstrate that relative to the fixed window strategy with markers, the proposed method has superior accuracy (an increase of 17.4 $\dot{\%}$ ) and an elevated information transfer rate (ITR) (rising by 24.9 bits/min), which can enhance the adaptability of online label-free BCI systems.