Binary program analysis techniques are widely applied in software security testing, malware analysis and detection, etc. Dynamic analysis is an important analysis method that can accurately show the running status of programs. However, it is confronted with some challenges, such as too high load during target program running and difficulty in dissecting the data structure information in detail. This study proposes a new data flow analysis method based on progressive expansion for binary programs. By taking full advantage of the ability of online data flow analysis, it focuses on the fine-grained analysis for partial program and expands the analysis range progressively to cover the entire program. The method utilizes a divide-and-conquer strategy that can reduce the performance impact on the runtime of the target program and thereby enable the execution of the target code segment sensitive to delay. Meanwhile, this study also presents a correlation analysis method for function parameters based on the memory reference relationship. It can detect the data flow propagation at the function call level and aid in the recovery of the internal data structures of parameters. In the end, this study shows the results of the experiments on the programs in the real environment, which suggest the feasibility and effectiveness of the proposed method. This method does not introduce significant extra analysis overhead while reducing the performance impact on the target program, capable of being applied in binary program analyses in practice.