High-precision clock synchronization is a fundamental technology enabling collaborative functions such as distributed sensing， formation control， and data fusion in general aviation swarms. However， in high-dynamic maneuvering scenarios， traditional round-trip time （RTT） synchronization methods suffer from significant accuracy degradation due to the coupling effects of relative motion-induced Doppler shifts and stochastic unequal reply time （URT） delays within airborne nodes. To address these challenges， this paper proposes a novel RTT clock synchronization algorithm that integrates relative-velocity compensation with a hybrid data-driven error correction mechanism. First， a kinematic model considering radial relative velocity is established to explicitly correct propagation delays caused by node mobility. Building on this， a batch-estimation-based delay modeling strategy is introduced. By extracting statistical features from multi-cycle timing data， this method calculates the equivalent processing delay sensitivity to eliminate systematic URT deviations. Furthermore， to address non-linear clock frequency drifts and complex environmental noise that traditional linear filters cannot resolve， a cascaded time-keeping architecture is developed. This architecture combines a Kalman filter （KF） for real-time state recursion with a Back-Propagation （BP） neural network for residual prediction. The BP network utilizes a lightweight topology to learn and compensate for non-linear errors based on inputs such as signal-to-noise ratio （SNR） and historical residuals. Extensive Monte Carlo simulations are conducted across continuous parameter spaces， including relative velocities up to 2 000 m/s and SNRs ranging from 4 dB to 20 dB. The numerical results demonstrate that the proposed algorithm achieves superior robustness and accuracy. Specifically， under strong URT interference （80 ns）， the synchronization error remains stable below 0.25 ns. In low-SNR environments （4 dB）， the root mean square error （RMSE） is controlled at approximately 0.2 ns， which represents a nearly tenfold improvement compared to the baseline.