In this project fundamental task for reusing content in cqa is to find similar questions for queried questions, as questions are the keys to accessing the knowledge in cqa. Then the best answers of these similar questions will be used to answer the queried questions. This is what we call question retrieval in this project. In this project existing system to the compare the ad hoc information retrieval, question retrieval in cqa has several advantages. First, the user can use natural language instead of only keywords as a query, and thus can potentially express his or her information need more clearly. Second, phase the system returns several possible answers directly instead of a long list of ranked documents, and can therefore increase the efficiency of finding the required answers.In this existing system, to address the lexical gap problem in cqa, previous work in the literature can be divided into two groups. The first group is the translation models, which leverage the question-answer pairs to learn the semantically related words for improving traditional IR models In this project, proposed system a flexible and effective re-ranking method, called CR-Re-ranking, to improve the retrieval effectiveness. To offer high accuracy on the top-ranked results, CR-Re-ranking employs a cross-reference (CR) strategy to fuse multimodal cues. Specifically, multimodal features are first utilized separately to re-rank the initial returned results at the cluster level, and then all the ranked clusters from different modalities are cooperatively used to infer the shots with high relevance. Experimental results show that the search quality, especially on the top-ranked results, is improved significantly.