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A web 3.0 cultural tourism marketing platform based on improved transformer recommendation model

computational-biology
adaptive-and-self-organizing-systems
artificial-intelligence
computer-education
software-engineering

Abstract

The digital transformation of cultural tourism requires intelligent algorithms capable of modeling heterogeneous and abstract user demands. This paper proposes a Web 3.0-based digital marketing framework for cultural tourism that integrates abstract element fusion analysis with an improved Transformer recommendation model. First, a hybrid architecture is designed, where a convolutional neural network (CNN) is employed to extract fused representations from abstract features (user preferences, intentions, expectations) and concrete features of cultural tourism resources. These fused representations are then processed by a Transformer with an enhanced self-attention mechanism incorporating relative positional encoding, which strengthens the correlation modeling between users and resource entities. Furthermore, a knowledge-graph-based relation scoring module is introduced to refine multi-entity recommendations. Experimental evaluations on the Tourism Statistics Database demonstrate that the proposed model achieves superior accuracy (90.23%), recall (90.67%), and F-measure (91.98%) compared with state-of-the-art baselines. Ablation studies verify the effectiveness of both the abstract element fusion module and the improved self-attention mechanism. Application-level simulations confirm that the system can provide real-time, precise, and personalized recommendations, thereby offering a scalable and robust solution for digital marketing in cultural tourism.
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