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The present study aims to identify suicide risks in major depressive disorders (MDD) patients from structural MRI (sMRI) data using deep learning. In this paper, we collected the sMRI data of 288 MDD patients, including 110 patients with suicide ideation (SI), 93 patients with suicide attempts (SA), and 85 patients without suicidal ideation or attempts (NS). And we developed interpretable deep neural network models to classify patients in three tasks including SA-versus-SI, SA-versus-NS, and SI-versus-NS, respectively. Furthermore, we interpreted the models by extracting the important features that contributed most to the classification, and further discussed these features or ROI/brain regions. • Suicidal behaviors among MDD patients could be identified by sMRI data. • Deep learning could be used to classify suicide attempts, ideation, and non-ideation in MDD patients. • The interpretable deep learning methods are helpful to improve the capability of suicide prediction among MDD patients. [ABSTRACT FROM AUTHOR]