Deep Integration System of Cultural and Tourism Resources Based on Machine Learning and Digital Twin Technology
Hua Tian · Procedia Computer Science · 2026
The utilization of cultural and tourism resources often suffers from insufficient data integration, homogenized experiences, and passive management responses. This research constructs a digital twin of cultural and tourism scenarios based on multi-source data fusion, integrating oblique photography, laser scanning, and IoT sensor data to establish a three-layer system architecture of "data-model-service". A CNN-LSTM-based tourist behavior recognition model and an LSTM Seq2Seq crowd prediction model were deployed at the model layer. Deep reinforcement learning was used to construct a virtual intelligent tour guide, achieving a behavior recognition accuracy of 94.73% and an average prediction RMSE of 0.09. The service layer supports bidirectional virtual-real driving and intelligent service encapsulation through a digital twin engine. Experiments show that the system achieves an average virtual-real synchronization latency of 1.62 seconds, effectively improving the in-depth utilization and intelligent management level of cultural and tourism resources.