Deep Integration System of Cultural and Tourism Resources Based on Machine Learning and Digital Twin Technology

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.