Dynamic community detection as an extension of static community detection, aims to identify similar and closely related sets of nodes across different snapshots and track the evolution of these sets over time, thus revealing the community structure and dynamic changes in dynamic networks. However, the characteristics of high noise and outliers in dynamic networks make traditional Non-negative Matrix Factorization (NMF) unable to maintain robust stability across continuous snapshots. Moreover, for dynamic networks that change complexly over time, it is important to effectively capture and utilize historical information. To address these issues, this paper introduces self-supervised learning. This paper proposes a self-supervised Symmetric Nonnegative Matrix Factorization temporal network community evolution exploration framework (S³ONMF). This model performs temporal coupling between sequences based on multiple random initializations and adaptive weighting, and uses a self-supervised method to screen the dynamic similarity matrix. This effectively resolves the long-standing trade-off between single-snapshot accuracy and temporal consistency. Finally, this Model explores the network evolution patterns between different network snapshots through the local-global evolution pattern (LEP-GEP). Experiments conducted on two types of synthetic dynamic networks and two types of real dynamic networks demonstrate that the proposed framework outperforms state-of-the-art models in dynamic community detection.