This　study　proposes　a　multimodal　feature　alignment　network　for　early　prediction　of　new-onset　conduction　disturbance　after　the　Transcatheter　Aortic　Valve　Replacement　(TAVR)　surgery.　Based　on　medical　prior　knowledge,　the　baseline　clinical　information,　calcification　in　the　aortic　root　complex　region,　radionics　features,　and　the　CT　slices　,　are　used　as　multimodal　data,　and　feature　selection　methods　are　used　to　select　features　that　are　highly　correlated　with　the　disease.　Then,　by　using　multimodal　feature　alignment　and　cross　attention　mechanisms　between　patterns,　the　data　from　multiple　patterns　is　fully　fused　together　to　achieve　information　complementarity　and　prediction　completion.　Specifically,　cosine　similarity　is　employed　to　measure　the　similarity　between　image　features　and　composite　features　in　the　feature　space　and　perform　feature　alignment.　Then,　a　cross-attention　mechanism　is　employed　to　enhance　the　interdependence　between　composite　features　and　image　features,　improve　the　predictive　performance　and　interpretability　of　the　model,　and　effectively　integrate　multimodal　features.　Experimental　results　show　that　our　method　achieved　90.48%,　84.21%,　94.12%,　and　88.00%　in　accuracy,　precision,　recall,　and　specificity,　respectively.　As　far　as　we　know,　we　have　not　found　similar　reports　on　this　topic.　The　proposed　method　provides　a　novel　approach　for　predicting　complications　after　TAVR,　which　is　with　great　significance　in　clinical　practice　of　the　cardiac　surgery.　©　2024　ACM.