The overpressure waveforms are disturbed，missing，or distorted in the field measurements of explosive shock waves，resulting in the failure to extract the feature values of shockwave.Therefore，a deep learning signal restoration and evaluation method that integrates local morphological features with global temporal dependencies is proposed.This method takes the incomplete sequences and binary missing masks as inputs，employs one-dimensional convolution to capture the microsecond-level abrupt changes in the shock wave front，and uses a cascaded bidirectional long short-term memory network with a self-attention mechanism to reconstruct the long-range attenuation trends.To overcome the underestimation of peak caused by traditional regression，the quantile regression and a dynamic peak-weighted loss function are jointly used for optimization，and the temporal generative adversarial networks are employed for sample augmentation to improve the generalization capability.The shockwave data measured during the damage process is processed.The results indicate that the proposed method effectively suppresses peak clipping，significantly reducing the absolute errors of peak overpressure，positive phase duration and specific impulse； at a 90% confidence level，the coverage rate of the predictive interval reaches over 0.94，and the interval width adaptively increases according to the degree of local information loss.The study demonstrates that the proposed method not only faithfully reconstructs the strong transient physical parameters，but also provides the quantitative metrics for restoration quality，offering highly reliable data support for the assessment of weapon damage effectiveness and the interpretation of engineering risk.