In this study, we propose a user behavior-based query optimization strategy to address the commonly observed suboptimal query performance and user experience in the field of video retrieval. This strategy primarily involves analyzing user behavioral data during the video retrieval process to expand query keywords, enhancing their relevance and diversity, thus optimizing search results. We achieved this by augmenting the original dataset to generate simulated user behavioral data and Ground Truth data. Subsequently, an automated query expansion algorithm effectively applied this data to video retrieval. Through experimental assessment on the video retrieval dataset (MSR- VTT), we validate the effectiveness of this method. The results demonstrate that this user behavior- driven query expansion approach significantly improves the accuracy of video retrieval and user satisfaction.