Abstract: Wireless intelligent sensors combined with cloud storage technology can realize long-term health monitoring for structures. Modal identification is an important part for structural health monitoring. The Hilbert-Huang transform (HHT) is widely used for structural modal identification because it is suitable for nonlinear and non-stationary signals and is self-adaptable. The algorithm of modal identification in the long-term monitoring cannot rely on subjective parameter selection, while the first step of the traditional HHT may produce false intrinsic mode function (IMF) components. The identification and elimination of false IMF components often rely on subjective judgment. In this paper, a new algorithm based on deep neural networks (DNN) and Kullback-Leibler (K-L) divergence is proposed, which can automatically identify and eliminate the false components generated by empirical mode decomposition (EMD).