This paper presents a Multi-Dimensional Network model aimed at analyzing mutually coupled membrane fouling in membrane bioreactors (MBR), addressing the limitations of existing diagnostic methods. First, fouling data were processed through wavelet transformation, using time-domain information as the network input. Second, initial features were extracted via convolution, with an inception structure applied for multidimensional feature extraction. Third, horizontal dense blocks were utilized to reduce network complexity while preserving key information. An attention mechanism was then incorporated to enhance model expressiveness, generalization, and diagnostic stability. Comparative testing revealed strong overall performance in diagnosing membrane fouling in hollow fiber membrane systems, achieving an average accuracy of 99.44%, which increased to 99.72% under noisy conditions. This underscores the model’s effectiveness in filtering contaminants in complex environments while maintaining excellent noise resistance and stability. Ultimately, the model significantly reduces operational and maintenance costs of wastewater treatment, providing a valuable theoretical foundation for advancing membrane water treatment technologies.