In the field of Binary Code Similarity Detection (BCSD), when dealing with functions in binary form, the conventional approach is to identify a set of functions that are most similar to the target function. These similar functions often originate from the same source code but may differ due to variations in compilation settings. Such analysis is crucial for applications in the security domain, involving vulnerability discovery, malware identification, and more. Function inlining, an optimization technique employed by compilers, embeds the code of called functions directly into the calling function. Due to different compilation options (such as O1 and O3) leading to varying levels of function inlining, this results in significant discrepancies between binary functions derived from the same source code under different compilation settings, posing challenges to the accuracy of state-of-the-art (SOTA) learning-based binary code similarity detection (LB-BCSD) methods. In contrast to function inlining, code extraction technology can identify and separate duplicate code within a program, replacing it with corresponding function calls. To overcome the impact of function inlining, this paper introduces a novel approach, CodeExtract. This method initially utilizes code extraction techniques to transform code introduced by function inlining back into function calls. Subsequently, it actively inlines functions that cannot undergo code extraction, effectively eliminating the differences introduced by function inlining. Experimental validation shows that CodeExtract enhances the performance of LB-BCSD models by 20% in addressing the challenges posed by function inlining.