Source code summarization is designed to automatically generate precise summarization for natural language, so as to help developers better understand and maintain source code. Traditional research methods generate source code summaries by using information retrieval techniques, which select corresponding words from the original source code or adapt summaries of similar code snippets; recent research adopts machine translation methods and generates summaries of code snippets by selecting the encoder-decoder neural network model. However, there are two main problems in existing summarization generation methods: on the one hand, the neural network-based method is more friendly to the high-frequency words appearing in the code snippets, but it tends to weaken the processing of low-frequency words; on the other hand, programming languages ??are highly structured, so source code cannot simply be treated as serialized text, or otherwise, it will lead to loss of contextual structure information. Therefore, in order to solve the problem of low-frequency words, a retrieval-based neural machine translation approach is proposed. Similar code snippets retrieved from the training set are used to enhance the neural network model. In addition, to learn the structured semantic information of code snippets, this study proposes a structured-guided Transformer, which encodes structural information of codes through an attention mechanism. The experimental results show that the model has significant advantages over the deep learning model generated by the current cutting-edge code summarization in processing low-frequency words and structured semantics.