In the realm of network science, Closeness Centrality (CC) and Betweenness Centrality (BC) serve as pivotal metrics for deciphering structural significance and information flow dynamics within networks. These metrics are indispensable for applications such as community delineation and network resilience analysis; however, their calculation in extensive graphs presents substantial computational burdens. Although recent developments in approximation methodologies have alleviated some of these challenges, issues pertaining to processing duration and responsiveness to network alterations persist. In this study, we introduce the CNCA-IGE model, an inductive graph neural network-based encoder-decoder framework. The framework utilizes the degree centrality (DC) of nodes as input feature and is specifically designed to proximate the CC and BC of nodes in complex networks. Across diverse synthetic and real-world networks, the CNCA-IGE model outperforms state-of-the-art baselines in both efficiency and accuracy. This advancement holds potential for enhancing applications such as social network analysis and the optimization of communication networks.