General search engines often cause the topic-drift problem, which means that during the retrieval process, some of the retrieval results are independent to the domain keywords. We propose a web page re-ranking algorithm for a specific domain-the TSRR(Topic Sensitive Re-Ranking) algorithm to solve the problem from a specific perspective. TSRR establishes a vector model which is independent to page rank for a specific domain and a web page information model; then it combines the vector model and the web page information model to re-rank the search results in the retrieval process. TSRR's performance is evaluated based on the criteria of customer satisfaction and precision. Experiment results on the dataset crawled for specific domains show that TSRR is excellent in performance. Compared with the ranking algorithm from Lucene, TSRR can promote the customer satisfaction performance by 17.3% and the precision performance by 41.9% on average.