The resting state functional magnetic resonance imaging （rs-fMRI） technology is widely used for predicting and diagnosing brain diseases. Researchers have proposed a large number of diagnostic methods based on rs-fMRI functional connectivity networks， but these methods have problems such as not fully considering the complex dependency relationships of brain network data and high computational costs. To address the aforementioned issues， this paper proposes a brain disease diagnosis method based on the bi-directional selective state space model （SSM） framework， named as Bi-BrainSSM. This method designs an attentive bi-Mamba （ABM） module that combines multi-head attention mechanism with a bi-directional selective state space model to enhance the representational ability of brain functional connectivity networks and improve the computational efficiency of the model. It simultaneously introduces orthogonal clustering readout to solve the problem of information loss in traditional aggregation methods. This method is validated on the publicly available ABIDE autism dataset and REST-MDD depression dataset. Experimental results show that compared to other comparison methods based on rs-fMRI， Bi-BrainSSM achieves more accurate diagnostic performance， while also having higher computational efficiency and lower GPU memory occupancy.