Inter-shaft bearing fault diagnosis can greatly save maintenance costs and guarantee the normal operation of aero-engines. However, existing methods are limited in their ability to handle sensor signals with high dimensional and complex noise. To tackle the above problems, we propose a Mamba network structure with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and bidirectional feature fusion for aero-engine inter-shaft bearing fault diagnosis, called MD-BiMamba. Firstly, we utilize the CEEMDAN technique to decompose the sensor signals of inter-shaft bearings and extract the intrinsic modal function by improving the noise addition method and decomposition strategy. Then, bidirectional Mamba is designed to extract the bidirectional long-range correlation of the sequence to achieve global feature extraction and fast detection of fault types with linear complexity. Moreover, an adaptive attention fusion method is proposed to achieve bidirectional feature fusion. Finally, we use the label smoothing regularization strategy to optimize the cross-entropy loss and enhance the generalization performance. Experimental results on real inter-shaft bearing datasets and noisy datasets show that the proposed method achieves 99.88 % and 95.81 % accuracy in the standard dataset and −10 dB noise environment, respectively, which is superior to other remarkable methods, in addition to achieving lower model complexity.