The research of functional magnetic resonance imaging (fMRI) has revealed the coherent fluctuation of low frequency signals between anatomically separated brain regions，which indicates functional network. Brain functional networks help better understanding of brain function and diagnosis of mental disease，and methods for analyzing brain functional networks play an important role. The paper firstly reviews two categories of methods for functional networks analysis：hypothesis driven and data driven methods，and then presents the theory，advantages and shortcomings of the traditional methods，including region of interests (ROI) based method，voxel based method，independent component analysis (ICA)，principal component analysis (PCA) and clustering method. Furthermore，the paper highlights the recently proposed group information guided ICA method and semi-supervised learning based ROI selection method. Finally，future potential improvements of the methods are prospected.