Aiming at singular value of matrix decomposition and ill-posed problems in traditional batch processing principal component analysis (PCA) based operational modal analysis (OMA), an operational modal identification method based on self-iterative principal component extraction is proposed. Different from traditional batch processing PCA, which obtains all principal components by matrix decomposition one time, the proposed method can realize the identification of main contribution operational modals by self-iterative principal component extraction one by one. Theoretical analysis shows its lower time and spatial complexity than traditional batch processing PCA based OMA. The simulation results on simple beam datasets show that the self-iterative principal component extraction algorithm can identify effectively main contribution modals and natural frequency of linear time invariant structure from smooth and random response signals. And it has smaller time cost in the case of more response points and more sampling time in contrast with traditional methods.