Abstract: The online identification of power system transient stability based on the measured data is the basis for realizing the "deciding and controlling on time" purpose. However, the accuracy of this method is limited by the intermediate links such as the scene matching or the generator grouping. In order to identify the transient stability more effectively, based on the random matrix theory, this paper proposes a method for identifying the transient power angle stability of the power system based on the time-series spectral distribution characteristics of the measured data. Firstly, a data model is constructed according to the transient response characteristics of the power system, and an insertion matrix model is then constructed to reduce the influence of the historical data on the spectral distribution characteristics. Next, combining the spectral distribution theory and the augmented matrix method, the maximum eigenvalue deviation (MED) difference trajectory is obtained, representing the data correlation. The mapping relationship between the MED difference trajectory and the transient power angle stability is analyzed based on the physical mechanism, and the transient stability criterion is formulated from the data correlation to realize transient response characteristic analysis and transient power angle stability online identification based on measured data.