Vestibular　schwannoma　(VS)　is　the　most　prevalent　intracranial　schwannoma.　Surgery　is　one　of　the　options　for　the　treatment　of　VS,　with　the　preservation　of　facial　nerve　(FN)　function　being　the　primary　objective.　Therefore,　postoperative　FN　function　prediction　is　essential.　However,　achieving　automation　for　such　a　method　remains　a　challenge.　In　this　study,　we　proposed　a　fully　automatic　deep　learning　approach　based　on　multi-sequence　magnetic　resonance　imaging　(MRI)　to　predict　FN　function　after　surgery　in　VS　patients.　We　first　developed　a　segmentation　network　2.5D　Trans-UNet,　which　combined　Transformer　and　U-Net　to　optimize　contour　segmentation　for　radiomic　feature　extraction.　Next,　we　built　a　deep　learning　network　based　on　the　integration　of　1D　Convolutional　Neural　Network　(1DCNN)　and　Gated　Recurrent　Unit　(GRU)　to　predict　postoperative　FN　function　using　the　extracted　features.　We　trained　and　tested　the　2.5D　Trans-UNet　segmentation　network　on　public　and　private　datasets,　achieving　accuracies　of　89.51%　and　90.66%,　respectively,　confirming　the　model＇s　strong　performance.　Then　Feature　extraction　and　selection　were　performed　on　the　private　dataset＇s　segmentation　results　using　2.5D　Trans-UNet.　The　selected　features　were　used　to　train　the　1DCNN-GRU　network　for　classification.　The　results　showed　that　our　proposed　fully　automatic　radiomics　pipeline　outperformed　the　traditional　radiomics　pipeline　on　the　test　set,　achieving　an　accuracy　of　88.64%,　demonstrating　its　effectiveness　in　predicting　the　postoperative　FN　function　in　VS　patients.　Our　proposed　automatic　method　has　the　potential　to　become　a　valuable　decision-making　tool　in　neurosurgery,　assisting　neurosurgeons　in　making　more　informed　decisions　regarding　surgical　interventions　and　improving　the　treatment　of　VS　patients.　©　2025　The　Author(s)