Forecasting　opening　box-office　earnings　has　become　an　emerging　demand,　affecting　filmmakers＇　financial　decisions　and　promotional　efforts　by　advertising　studios　that　create　trailers.　Decision-makers　have　a　complex　and　challenging　task　due　to　a　large　amount　of　data　and　several　complex　considerations.　Based　on　deep　multimodal　visual　features　derived　from　trailer　content　and　a　cross-input　neighborhood　feature　fusion,　an　innovative　Deep　Multimodal　Predictive　Cross-Input　Neural　Network　model　(DMPCNN)　is　proposed　for　predicting　opening　movie　box-office　revenue.　DMPCNN　is　a　fully-connected　recurrent　neural　network　with　two　architectures:　A　Visual　Feature　Extraction　Model　(ResNet+LSTM)　block　for　extracting　and　learning　mid-level　temporal　visual　content　and　Cross-Input　Neural　Network　fusion　for　uncovering　and　fusing　high-level　spatial　features　in　trailers　to　predict　movie　revenue.　The　ResNet+LSTM　block　focuses　on　learning　various　trailer　segments,　while　the　Cross-Input　Neural　Network　simultaneously　learns　and　combines　features　from　movie　trailers　and　metadata　and　corresponding　similarity　metrics.　DMPCNN　aided　in　developing　a　decision　support　system　that　incorporates　useful　revenue-related　trailer　features.　We　evaluated　DMPCNN＇s　performance　on　the　Internet　Movie　Dataset　by　obtaining　metadata　for　50,186　movies　from　the　1990s　to　2022　and　comparing　it　with　different　state-of-the-art　frameworks.　The　erudite　features　in　trailers　and　the　predicted　results　outperformed　baseline　models,　achieving　81%　feature　precision　and　84.40%　accuracy.