Membrane fouling seriously restricts the application of ultrafiltration (UF) technology in municipal wastewater treatment. Secondary effluent organic matter (EfOM), characterized by its complex composition, is considered the primary cause of membrane fouling. However, there is no method to effectively identify the key components of membrane fouling. To address this, this paper proposed a key membrane fouling factor identification framework based on the tabular prior-data fitted network (TabPFN) deep learning model by using SHapley Additive exPlanations (SHAP) method. Water quality and membrane fouling data were collected from a modified ceramic ultrafiltration membrane (MCUM) during a two-hour operation cycle. UV₂₅₄, dissolved organic carbon (DOC), fluorescence regional integration values, and fluorescence component intensities (F_max) were selected as input variables to predict normalized specific flux, reversible resistance, and irreversible resistance. The results show that the TabPFN model achieves high-precision prediction without hyperparameter tuning (R²=0.97 for normalized specific flux), outperforming conventional machine learning models in terms of overall performance. Further improvement is obtained by introducing a post-hoc ensembling (PHE) strategy, increasing the prediction performance of R² to 0.98. SHAP analysis indicates that tryptophan-like, fulvic acid-like, and humic acid-like substances have predominant influences on the reduction of membrane flux and the formation of irreversible resistance and reversible resistance, respectively. PDPs results further reveal significant nonlinear synergistic effects among these components, especially the synergistic enhancement effect of the fulvic acid-like and aromatic substances in the irreversible fouling process. This study reveals the key components of secondary EfOM responsible for membrane fouling, providing theoretical support for membrane fouling control strategy optimization and the understanding of fouling mechanisms.