Research on Improving Rural Tourism Experience Model Using Natural Language Processing Technology

This paper proposes a set of algorithm frameworks based on natural language processing (NLP) technology, aiming to improve the accuracy and adaptability of rural tourism experience models. This algorithm framework is mainly used for tasks such as sentiment analysis, topic modeling and trend prediction. By deeply mining tourist feedback data, a rural tourism experience model that can accurately reflect tourists' subjective feelings and changes in demand is constructed. In terms of sentiment analysis, this paper combines the sentiment dictionary method with the latent Dirichlet allocation (LDA) model to analyze the sentiment tendency of tourists' text feedback, thereby revealing tourists' emotional fluctuations and preference trends. This paper also introduces a data fusion method to integrate data from multiple sources to reduce the interference of noise on the results and improve the reliability of prediction. The simulation results show that the rural tourism experience model based on NLP technology can effectively identify changes in tourist needs in practical applications, adjust tourism services and facilities in a timely manner, and thus improving the overall satisfaction of tourists. Specific experimental data show that the experience design after model optimization has increased tourist satisfaction by about 15% compared with the traditional method. Through real-time adjustment of the algorithm and personalized recommendation, the overall experience of tourists has been significantly improved.