Bash is the default shell command language for Linux, which plays an important role in the development and maintenance of Linux systems. Nevertheless, understanding the purpose and functionality of the Bash code is a challenging task. Therefore, an automatic method ExplainBash is proposed based on dual information retrieval for automatic Bash code comment generation. Specifically, the proposed method is based on semantic similarity and lexical similarity to perform dual information retrieval, which aims to generate high-quality code comments. For semantic similarity, CodeBERT and BERT-whitening operator are used to learn the code semantic representation, and Euclidean distance is resorted to compute semantic similarity; while for lexical similarity, code is represented as a set of code tokens, then the edit distance is resorted to compute lexical similarity. A high-quality corpus is constructed based on the corpus shared in the NL2Bash study and the data shared in the NLC2CMD competition. After that, nine state-of-the-art baselines are selected from the automatic code comment generation domain, which cover the information retrieval-based methods and deep learning-based methods. Results of empirical study and human study verify the effectiveness of the proposed method. Ablation experiments are also designed to analyze the rationality of the settings (such as retrieval strategy, BERT-whitening operator) in the proposed method. Finally, a browser plug-in is developed based on the proposed method to facilitate the code comprehension of the Bash code.