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 bidirectional selective state space model (SSM) framework, Bi-BrainSSM。This method designs an attentive bi-Mamba (ABM) module that combines multi head attention mechanism with a bidirectional selective state space model to enhance the representational ability of brain functional connectivity networks and improve the computational efficiency of the model. Simultaneously introducing orthogonal clustering readout to solve the problem of information loss in traditional aggregation methods. This method was validated on the publicly available ABIDE autism dataset and REST-MDD depression dataset. The 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.