Coherent cluster identification plays an important role in the dynamic equivalent and active splitting control of power systems. At present, coherent clustering mainly depends on the generator's power angle curve features. There are problems with single selection indicators and insufficient feature extraction. For these problems, this paper proposes a Coherent cluster identification plays an important role in the dynamic equivalent and active splitting control of power systems. At present, coherent clustering mainly depends on the generator's power angle curve features. There are problems with single selection indicators and insufficient feature extraction. For these problems, this paper proposes a coherent clustering method based on multi-index panel data feature extraction. First, it selects three indicators that can reflect the coherence of power angle increment, terminal voltage and rotor angular velocity of the generator to form panel data. Secondly, based on the characteristics of the panel data with both the cross-sectional and the time-series dimensions, it extracts the "absolute value", "fluctuation", "skew degree", "kurtosis" and “trend” features of the three indices in the time series. Then it builds a hierarchical analysis cluster model. It uses the AHP-entropy weight method to calculate the weight of each index and each feature, and calculates the weighted distance matrix between generator groups, to achieve the final coherent group by system clustering. Finally, the EPRI-36 node system and North China Power Grid are taken as examples to verify the effectiveness of the proposed method. This work is supported by Science and Technology Project of the Headquarter of State Grid Corporation of China (No. SGXJJY00GHJS1800003): “Study on Safety Quality Evaluation and Grid Strengthening Scheme Optimization of Weak Delivery Network with UHV/EHVDC”.