Many code files become oversized and take on excessive responsibilities as software evolves, which severely affects software maintainability and comprehensibility. Developers often need to refactor such files by decomposing a large code file into several smaller ones. Existing studies mainly focus on class file decomposition and are not fully applicable to decomposing complex header files. This is because header file decomposition faces unique challenges. It needs to consider the build dependencies of the entire software project to reduce compilation cost and ensure that the decomposed files are free of cyclic dependencies. To address these challenges, this study proposes an automated approach for decomposing and refactoring complex header files, HeaderSplit. It first constructs a code element graph that captures multiple types of code relationships, including co-usage relationships that reflect project build dependencies. Then, a node coarsening process and a multi-view graph clustering algorithm are applied to identify clusters of closely related code elements. A heuristic algorithm is further introduced to eliminate cyclic dependencies in the clustering results. After the decomposition plan is confirmed, HeaderSplit automatically performs the refactoring, generating new sub-header files and updating the include statements in all code files that directly or indirectly include the original header file. HeaderSplit is evaluated on both synthetic and real complex header files. The results are as follows. 1) HeaderSplit improves accuracy by 11.5% compared with existing methods and demonstrates higher cross-project stability. 2) The decomposed sub-files have higher Modularity and no cyclic dependencies, indicating better architectural design. 3) Using HeaderSplit to decompose complex header files can reduce recompilation costs in their evolution history by15%–60%. 4) HeaderSplit efficiently performs automated refactoring, completing the decomposition and refactoring of header files in large-scale software projects with millions of lines of code within five minutes, showing high practical value.