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Poor understanding of the underlying synthetic chemistry constrains the design of efficient synthesis routes toward the desired final products, especially those at atomic precision. Using the synthesis of atomically precise gold nanoclusters as a demonstration platform, a deep learning framework for guiding material synthesis is successfully developed to accelerate the workflow. With only 54 examples, the graph convolutional neural networks (GCNN) plus siamese neural networks (SNN) classification model is trained. The prediction capability is demonstrated with the successful prediction of literature\u2010reported protocols. In addition, understanding of the synthesis process can be acquired from a decision tree trained by plentiful generated data from a well\u2010trained classification model. 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