Aerospace software presents significant challenges to requirements engineering due to its design complexity and stringent safety standards. When manually drafting requirement documents, engineers need strong domain knowledge while also navigating heterogeneous data, which leads to errors and inefficiencies. This paper evaluates the capabilities of large language models (LLMs) in understanding aerospace software requirements and their potential to assist in requirements question answering (QA). We develop an aerospace requirements QA benchmark based on industrial software assets, books, and research materials, creating a total of 6,696 QA pairs across ten tasks and three heterogeneous data formats: text, tables, and formulas. We then evaluate the domain-specific performance of five mainstream open-source LLMs using zero-shot learning, few-shot learning, and retrieval-augmented generation (RAG) techniques. We further categorize hallucinations from LLMs and quantitatively analyze error distributions. Moreover, we conduct a user study to assess the LLM’s practical usefulness when applying to requirements QA. The evaluation results show that (1) LLMs demonstrate limited performance in the aerospace software domain, (2) RAG techniques significantly enhance the capabilities of LLMs for text-based tasks, while few-shot learning improves the performance of most LLMs, (3) four distinct types of QA hallucinations are identified, and (4) LLM QA is particularly beneficial for junior engineers. This research provides valuable perspectives for the future application of LLMs in aerospace software.