Model-merging has emerged as a powerful approach in deep learning, capable of enhancing model performance without any training. However, the underlying mechanisms that explain its effectiveness remain largely unexplored. In this paper, we investigate this technique from three novel perspectives to empirically provide deeper insights into why and how weight-averaged model-merging~\cite{wortsman2022soups} works: (1) we examine the intrinsic patterns captured by the learning of the model weights, and we are the first to connect that these weights encode structured with why weight-averaged model merging can work; (2) we investigate averaging on weights versus averaging on features, providing analyses from the view of diverse architecture comparisons on multiple datasets; and (3) we explore the impact on model-merging prediction stability in terms of changing the parameter magnitude, revealing insights into the way of weight averaging works as regularization by showing the robustness across different parameter scales. The code is available at https://github.com/billhhh/Rethink-Merge.