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Traditional code vulnerability detection techniques have limited detection efficiency and accuracy. Deep learning techniques have recently gained distinct advantages in multidimensional feature extraction and large-scale data processing, and their application in code vulnerability detection is evolving from simple classification to multimodal approaches. This paper primarily systematizes and summarises deep learning-based source code vulnerability detection, as well as analyzes and anticipates current challenges and future research directions in this area. The distinction between this review and the preceding reviews: This study investigates the literature of the last four years; Not only does it contain datasets, but also includes model-related research and an analysis of multiple different application scenarios. 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