This paper introduces a novel approach for estimating structural break ratios in the factor loadings of high-dimensional approximate factor models, where the breaks occur at unknown common dates and the number of factors is unknown. Our method is based on the observation that the sum of the numbers of pseudo factors in the pre- and post-split subsamples is minimized when the sample is split at the structural break. By appropriately transforming these criteria using the eigenvalue ratios of the covariance matrices of the pre- and post-split subsamples, we derive consistent estimators for the structural break ratios. Notably, our framework exhibits remarkable flexibility in accommodating weak factors and can be easily extended to handle multiple breaks. We also introduce a data-driven process to determine the number of breaks. Monte Carlo simulations demonstrate good performance of the proposed estimators. Furthermore, in an empirical analysis of the FRED-MD dataset, we identify two structural breaks around January 1983 and March 2009.