让自动化测试能识别网页交互中的无障碍缺陷，提升检测准确率。

Modern web applications increasingly expose accessibility barriers through interaction flows rather than static page snapshots. Keyboard traps, focus loss, modal leakage, delayed status updates, dynamic controls, and changing page regions often become observable only after users perform concrete actions. These behaviors are directly related to dynamic WCAG criteria, yet they remain difficult to automate because their assessment depends on runtime interaction evidence and is still commonly performed through manual inspection. We present Flow-A11y, a flow-aware accessibility testing system for interaction-dependent WCAG criteria. Given a target page and a natural-language scenario, Flow-A11y executes the flow in a real browser, records an ordered runtime trace, constructs criterion-specific evidence packets, gates unsupported judgments, and emits auditable findings grounded in resolvable runtime evidence. Evaluated on 19 real public-web scenarios covering 45 dynamic WCAG criteria, Flow-A11y achieves over ten times higher oracle agreement than a generic browser-agent audit, while its evidence-calibration layer improves fail precision from 23.5% to 41.4% and eliminates invalid evidence references. These results show that runtime traces provide actionable evidence for assessing interaction-dependent accessibility behavior. They demonstrate a practical path toward automating dynamic WCAG criteria that page-level scanners cannot assess and that have traditionally required manual evaluation.