In recent years, large language model technology has been widely used, but its reasoning ability still has significant limitations. Especially when dealing with more complex logical reasoning problems, the accuracy of large language model reasoning is often unable to meet the requirements, and the resolution method can better ensure the accuracy of reasoning. In this paper, we propose Bi-Resolution, a novel method for reasoning about large language models. We introduce bidirectional reasoning into the improved resolution method and implement an automated reasoning process based on the generation of large language models through the design of prompt words. Technically, Bi-Resolution first converts the natural language problem into a symbolic representation of first-order logic, and selects the corresponding version of resolution algorithm according to the predicted reasoning result. This method can help the large language model to more accurately judge the reasoning problem with the conclusion of "not entirely true and not entirely false". In the process of resolution, the idea of bidirectional reasoning is used to instantiate the constraint variables, which removes the redundant conditions in the reasoning problem and reduces the complexity of reasoning. We conducted experiments on the FOLIO dataset, and the results show that the Bi-Resolution architecture successfully improves the accuracy of large language model reasoning.