The　dissemination　of　fake　news,　often　fueled　by　exaggeration,　distortion,　or　misleading　statements,　significantly　jeopardizes　public　safety　and　shapes　social　opinion.　Although　existing　multimodal　fake　news　detection　methods　focus　on　multimodal　consistency,　they　occasionally　neglect　modal　heterogeneity,　missing　the　opportunity　to　unearth　the　most　related　determinative　information　concealed　within　fake　news　articles.　To　address　this　limitation　and　extract　more　decisive　information,　this　article　proposes　the　modality　perception　learning-based　determinative　factor　discovery　(MoPeD)　model.　MoPeD　optimizes　the　steps　of　feature　extraction,　fusion,　and　aggregation　to　adaptively　discover　determinants　within　both　unimodality　features　and　multimodality　fusion　features　for　the　task　of　fake　news　detection.　Specifically,　to　capture　comprehensive　information,　the　dual　encoding　module　integrates　a　modal-consistent　contrastive　language-image　pre-training　(CLIP)　pretrained　encoder　with　a　modal-specific　encoder,　catering　to　both　explicit　and　implicit　information.　Motivated　by　the　prompt　strategy,　the　output　features　of　the　dual　encoding　module　are　complemented　by　learnable　memory　information.　To　handle　modality　heterogeneity　during　fusion,　the　multilevel　cross-modality　fusion　module　is　introduced　to　deeply　comprehend　the　complex　implicit　meaning　within　text　and　image.　Finally,　for　aggregating　unimodal　and　multimodal　features,　the　modality　perception　learning　module　gauges　the　similarity　between　modalities　to　dynamically　emphasize　decisive　modality　features　based　on　the　cross-modal　content　heterogeneity　scores.　The　experimental　evaluations　conducted　on　three　public　fake　news　datasets　show　that　the　proposed　model　is　superior　to　other　state-of-the-art　fake　news　detection　methods.