Design of a Digital Exhibition Service System under the Deep Belief Network Models

This work aims to optimize the classification efficiency of the digital exhibition service system and achieve optimization of booth layout and visitor route planning. This work combines the Deep Belief Network (DBN) model with Reinforcement Learning (RL) algorithms and Random Forest (RF) algorithms to design and construct a digital exhibition service system. This work utilizes publicly available exhibition promotion and display channels, selecting four common types of exhibitions such as industry exhibitions. Each type chooses 10 different time periods and content formats of exhibition data, which are scattered and arranged into five exhibition datasets. The work introduces the RF algorithm as an auxiliary classifier, extracts the features through the DBN model, and uses quantitative indicators to evaluate the robustness of the model and the accuracy and personalization of the recommended results. Meanwhile, the learned features and patterns are input into the RL algorithm to verify the system’s decision optimization effect. Results demonstrate that (1) Under perturbed data conditions, the system’s accuracy average differs by only 0.4% from the original data conditions; the average F1 score differs by 0.003; and the average recall rate differs by only 0.1%. This indicates that the system exhibits good robustness when facing perturbed environments. (2) Cross-validation results show that the system maintains stable classification efficiency across different folds, with accuracy ranging from 82% to 89%. The average time consumption for each fold does not exceed 10ms, indicating that the system can efficiently classify different types of exhibition data. (3) Variance analysis results show that the p-values corresponding to five indicators—recommendation accuracy, recommendation coverage rate, personalized recommendation effect score, recommendation click-through rate, and user satisfaction—are 0.036, 0.027, 0.037, 0.046, and 0.039, respectively. They are all less than 0.05, indicating that the system has significant value in use and performs superiorly in personalized recommendations. (4) Decision-effect verification results show that the system’s decision accuracy is highest at 96.1% for consumer goods exhibitions, with a reduction rate of 71.2%. The decision effects of the other three types of exhibitions have also been significantly optimized, indicating that while maintaining relatively high accuracy, the system can improve decision optimization by effectively reducing key errors. This work aims to ensure real-time decision-making strategies and improve the accuracy of personalized matching in digital exhibition service systems, providing more accurate and efficient service experiences for participants of different types of exhibitions.