To promote the creative transformation and innovative development of excellent traditional culture (the two–culture concept), to facilitate the deep integration of agriculture, commerce, tourism, and culture is a major path for regional high–quality development. However, the traditional methods have difficulty in dealing with cultural data that are heterogeneous, massive and multi–source industrial data such as semantic gaps and low integration efficiency. This study constructs an enabling mechanism analysis framework based on a multimodal deep learning model, it systematically studies how this model can digitally activate cultural resources through cross–modal semantics, knowledge graph construction, generative recommendation to achieve intelligent matching and collaborative value–added of agricultural, commercial and tourism elements. Empirical Analysis Research uses typical Cultural Eco–Protection Zone as Case Area, collect five type multimodal Datasets(Sample Size N=12847).Text,image,voice,video,Spacetime economy Data.By building a contrastive language image pre training(CLIP) + Transformer fused Model,the effects of the model on the enabling of three dimensions,cultural symbol extraction,consumer preference prediction, industrial chain optimization,were quantitatively analyzed. Results show: ① The accuracy of the recognition of cultural elements by the multimodal model reaches 89.3%, which increases by 22.6% compared with the traditional single–mode method. ②The cooperative index of Agriculture and Commerce driven by the model increases from 0.324 during the baseline period to 0.687 in the intervention period, which grows by 112%. ③The mechanism analysis shows a four–level transmission path according to attention:”cultural genes–product innovation–scene experience–value return”. This paper is the first to explore the quantitative mechanism of enabling industrial integration between cultural two–culture through the perspective of deep learning, providing computable and transferable methodology support for regional smart tourism and rural revitalization.
