In order to extract structural information from the bridge structural dynamic signal with high noise level, a novel adaptive decomposition and reconstruction method is proposed by combining the ensemble empirical mode decomposition (EEMD) and principal component analysis (PCA) for the specific characteristics of bridge structural dynamic signals. Based on the in-depth analysis of mode mixing in empirical mode decomposition, the uniformity of probability density function for white noise is adopted to improve the pattern one of mode mixing, and the correlation analysis is used to ameliorate the pattern two of mode mixing, then the calculation efficiency and decomposition accuracy are upgraded greatly in the improved EEMD. The multi-scale principal components analysis is implemented on all of the intrinsic mode functions (IMFs) obtained by the improved EEMD for noise reduction and selection of IMFs. Moreover, the dynamic signal is reconstructed. The effectiveness of the proposed method is verified by both of the simulated signal and testing signal from real bridge structure. The verified results showed that the proposed method can decompose adaptively and denoise effectively the bridge dynamic signal with high noise, and can extract accurately the structural information from the testing signal, furthermore it is applicable in the dynamic testing analysis of real bridge structure.