Bug location based on information retrieval (IR) uses cross language semantic similarity to construct a retrieval model to locate source code errors through bug report. However, the traditional method of bug location based on IR treats the code as pure text and only uses the lexical semantic information of source code, which leads to the problem of low accuracy caused by the lack of candidate code semantics in fine-grained bug location, and the usefulness of the results needs to be improved. By analyzing the relationship between code change and bug generation in the scenario of program evolution, this study proposes a fine-grained bug location method based on source code extension information, the explicit semantic information of code vocabulary and implicit information of code execution are used to enrich source code semantics to realize fine-grained bug location. Based on the location candidate points, the semantic context is used to enrich the code quantity, and the structural semantics of code execution intermediate language is used to realize fine-grained code distinguishability. Meanwhile, natural language semantics is used to guide the generation of code language representation based on attention mechanism, the semantic mapping between fine-grained code and natural language is implemented to implement fine-grained bug location method FlowLocator. The experimental results show that compared with the classical IR bug location method, the location accuracy of this method is significantly improved in the Top-N rank, mean average precision (MAP) and mean reciprocal rank (MRR).