Large language models (LLMs) have made significant progress in software engineering (SWE) tasks, such as code generation and automatic repair. However, current SWE agents have limited effectiveness when handling complex software defects and often overlook potentially useful information in failed patches. To address this, this study proposes an automatic program repair (APR) framework called DeIMerge, which is based on multi-agent collaboration and intelligent patch merging. After voting to select the optimal patch, the framework uses an LLM to analyse all failed patches deeply, fuse scattered repair clues, and generate high-quality merged patches. Experimental results show that this method increases the single-patch repair rate of open-source SWE agents from 27.3% to 37.0%, with an overall maximum repair rate of 57.3%. This framework has been validated for its versatility across multiple mainstream LLM models. Multi-agent patch merging can effectively extract repair clues from failed patches and significantly improve automatic repair performance, providing new insights into solving complex defects.