Abstract: Knowledge base question answering is a method of retrieving relevant information from the knowledge base for model inference, finally return accurate and concise answers. In recent years, with the development of deep learning and large language models, knowledge base question answering based on information retrieval has become a focus, and many novel research methods have emerged. We summarize and analyze the methods of knowledge base question answering based on information retrieval from different aspects such as model methods and datasets. Firstly, we introduce the research significance and related definitions of knowledge base question answering. Then, according to the model execution process, explain the key problems and typical solutions faced in each stage from four stages: question parsing, information retrieval, model inference, and answer generation, and summarize the common network modules used in each stage. Then, starting from the overall model, analyze and sort out the inexplicability of knowledge base question answering based on information retrieval methods. In addition, relevant datasets with different characteristics and baseline models at different stages are classified and summarized. Finally, the summary and outlook are provided on each execution stage of knowledge base question answering based on information retrieval, as well as the overall development direction of the field.