Tracking the evolution of communities (or clusters) in dynamic networks is a critical challenge in numerous applications, including social network analysis, biological systems, and financial modeling. Existing methods primarily focus on node membership overlap while overlooking structural and attribute-based transformations, leading to inconsistencies when clusters undergo structural or attribute changes. To address these limitations, we propose TrackGAE, a two-phase deep learning framework that leverages graph autoencoders to generate temporal representations of clusters and construct evolutionary sequences that preserve community identity. In the first phase, a Temporal Graph Autoencoder extracts structural and attribute-aware cluster embeddings. In the second phase, a Clustering Graph Autoencoder refines these embeddings using a proposed Deep-Pruning mechanism to generate high-quality cluster sequences. TrackGAE captures node membership, attributes, and structure, enabling accurate tracking of dynamic clusters over time. Preliminary results on the Yelp dataset demonstrate the suitability of our approach.