Major Depressive Disorder (MDD) is a prevalent and severe mental disease. Functional Magnetic Resonance Imaging (fMRI)-based diagnostic methods, which analyze Functional Connectivity (FC) to identify abnormal functional connections, have shown promise as biomarker-based approaches for diagnosing depression. However, the high costs of fMRI data result in small sample sizes, hindering the effective identification of abnormal FC patterns. Moreover, existing methods often overlook the potential benefits of incorporating domain knowledge into their models. In this paper, we propose KnowMDD, a novel knowledge-guided cross contrastive learning framework for MDD diagnosis. By incorporating domain knowledge and employing data augmentation, KnowMDD addresses data sparsity while improving robustness and interpretability. Specifically, multiple atlases are used to construct complementary brain graph representations. The default mode network, closely associated with depression, is introduced into the contrastive learning paradigm for diverse subgraph augmentations, while an attention mechanism captures global semantic relationships between brain regions. Based on them, a cross contrastive learning is designed to learn robust representations for accurate diagnosis. Extensive experiments demonstrate the effectiveness, robustness, and interpretability of KnowMDD, which outperforms state-of-the-art methods. We also develop a demonstration system to show its practical application.