Source code migration techniques are designed to convert source code from one programming language to another, which helps reduce developers’ burden in migrating software projects. Existing studies mainly use neural machine translation (NMT) models to convert source code to target code. However, these studies ignore the code structure features, resulting in poor source code migration performance. Therefore, this study proposes a source code migration model based on a code-statement masked attention Transformer (CSMAT). The model uses Transformer’s masked attention mechanism to guide the model to understand the syntax and semantics of source code statements and inter-statement contextual features when encoding and make the model focus on and align the source code statements when decoding, so as to improve migration performance of source code. Empirical studies are conducted on the real project dataset, namely CodeTrans, and model performance is evaluated by using four metrics. The experimental results have validated the effectiveness of CSMAT and the applicability of the code-statement masked attention mechanism to pre-trained models.