Abstract: Major Depressive Disorder (MDD) is a mental illness that severely impairs human health. Currently, depression recognition based on Contrastive Learning (CL) for Electroencephalogram (EEG) signals has achieved promising results, yet it still has certain limitations: mainstream CL methods mainly focus on the temporal consistency of EEG signals while neglecting the topological consistency of EEG data, namely the anti-interference stability of individual channels and the inherent neurophysiological correlations between channels. To address the above issues, this paper proposes a novel depression recognition model based on Temporal-Topological consistency Graph Contrastive Learning (TTGCL). First, an adaptive graph augmentation strategy is designed, which includes dynamic node augmentation based on signal-to-noise ratio and frequency-domain energy distribution, as well as edge augmentation based on dynamic regular constraints, to generate high-quality contrastive views. Second, Cross-Window Temporal Consistency Contrast (CWTCC) is proposed to learn the temporal consistency of EEG signals through cross-prediction between dual views. Finally, a Regional-Global Graph Contrast (RGGC) framework is constructed to learn topological consistency from the level of local brain regions to global samples. This study achieves recognition accuracies of 96.53% and 96.33% on two public MDD datasets, respectively, outperforming existing baselines.