<p>This paper investigates the data-driven event-triggered control problem for unknown continuous-time linear systems. To reduce unnecessary data transmissions, a topology-aware dynamic event-triggered mechanism is proposed. Unlike strategies that rely solely on the magnitude of the measurement error, the proposed design incorporates the topological relationship between measurement errors and system states by integrating the Lyapunov-function matrix into the triggering condition. By identifying when the directional state-error interaction is favorable, the mechanism automatically decelerates the decay of the dynamic threshold, thereby extending inter-event intervals. A robust design framework is established using noisy offline data, where both the controller gain and triggering parameters are jointly determined via linear matrix inequalities (LMIs). Theoretical analysis guarantees exponential input-to-state stability (ISS) and excludes Zeno behavior. Simulation results validate the effectiveness of the method.</p>