Knowledge graph is one of the key technologies to realize question answering in open domain. Open domain question answering tasks often require enough knowledge information, and the incompleteness of knowledge graph becomes an important factor restricting the performance of question answering system. When combining external unstructured text with structured knowledge based on knowledge graphs to fill in missing information, the accuracy and efficiency of retrieving external texts are particularly critical, and selecting texts that are highly relevant to the problem can improve system performance. Conversely, selecting texts that are less relevant to the question will introduce knowledge noise, thereby reducing the accuracy of question answering tasks. Therefore, this paper designs a question answering system model that integrates text and knowledge graph, in which the text retriever can fully mine the semantic information of questions and texts to improve the quality of retrieval and the accuracy of query subgraphs. The knowledge mixer can combine knowledge from text and knowledge bases to build fusion representations of knowledge. The experimental results show that the proposed model has certain advantages in performance compared with the comparison models.