Recent advancements in reasoning over Temporal Knowledge Graphs have leveraged historical data to forecast future events more effectively. Traditional models primarily rely on the recurrence and periodicity of events, using past occurrences to predict future ones. These methods often use a self-relation mechanism to account for the influence of timestamps in predictions but typically overlook the significance of interconnected entities within the temporal framework. Addressing this oversight, we introduce a new model called CA-GCN, which is based on a relational graph convolution network. This model not only taps into historical data through a self-attention mechanism but also integrates previously unseen static information. It further extracts insights from the graph’s structure using contrastive learning techniques. The embeddings generated by our model are utilized to train a linear binary classifier, aimed at identifying entities crucial for future predictions. Our model demonstrates substantial improvements, showing up to a 5.78% increase in Mean Reciprocal Rank (MRR) and a 10.88% rise in Hits@1 accuracy, when tested across several standard datasets such as ICEWS14, ICEWS18, YAGO, and WIKI. These results indicate that CA-GCN significantly outperforms existing models, providing enhanced predictive accuracy in various evaluation metrics.