Abstract: Feature selection is very important in improving the performance of learning systems. Various feature selection algorithms greatly facilitate the research of the scientists from different disciplines, however, there is a common problem that those algorithms are implemented by different researchers, so it is hard for the users to integrate or compare those independent implementations of different programming styles and incompatible designs. A feature selection library based on strategy-pattern (FSLS) is conceived to solve the above problem. The FSLS encapsulates many popular feature selection algorithms under unified interfaces, while different strategies of one algorithm could be exchanged conveniently. This library will bring great help to those machine-learning algorithms users. Meanwhile, the FSLS itself has good extensibilities, and new algorithm can be added into the library easily.