In modern information retrieval systems, effective indexing can be achieved by removal of stop words. Till now many stop word lists have been developed for English language. However, no standard stop word list has been constructed for Chinese language yet. With the fast development of information retrieval in Chinese language, exploring Chinese stop word lists becomes critical. In this paper, to save the time and release the burden of manual stop word selection, we propose an automatic aggregated methodology based on statistical and information models for extraction of the stop word list in Chinese language. The novel algorithm balances various measures and removes the idiosyncrasy of particular statistical measures. Extensive experiments have been conducted on Chinese segmentation for illustration of its effectiveness. Results show that the generated stop word list can improve the accuracy of Chinese segmentation significantly.