Optimization and Intelligent Evaluation of Recreation Design for Jingdezhen Ceramic Industry Heritage

Abstract

As an important carrier of China's porcelain capital culture, the recreational experience design of Jingdezhen's ceramic industry heritage is directly related to the revitalization and utilization effect of cultural heritage. Based on multi-source sensor networks to collect tourist behavior data, an evaluation index system was constructed that includes three dimensions: cultural value, spatial experience, and service quality. Deep reinforcement learning methods were used to optimize the spatial layout of 186 functional units, achieving an optimization effect of extending tourist stay time by 46 minutes and increasing satisfaction by 0.85 points. A virtual real fusion tourism system was developed, with an accuracy rate of 96.3% for tour guidance and explanation, and an average browsing depth increase of 2.8 times for users. Practice has shown that this optimization scheme effectively improves the recreational experience quality of industrial heritage, which is of great significance for promoting the protection and utilization of industrial heritage.