Integration of Bi-LSTM for Film Tourism Scene Perception and Tourist Satisfaction under Artificial Intelligence

For the specific task of analyzing tourist satisfaction in film tourism scenarios, this study proposes a deep learning model that integrates multiple technologies. The model takes contextual word vectors as input, which are generated by a pre-trained language model. It combines Bidirectional Long Short-Term Memory (Bi-LSTM) to capture contextual semantic dependencies in text, while an attention mechanism is employed to dynamically focus on key emotional expressions. In addition, structured domain features, including film and television work tags and location entities, are incorporated to enhance the model's ability to understand film and television contexts. Experiments are conducted on a film tourism review dataset. The results show that the proposed method performs excellently in the three-classification task of positive, neutral, and negative emotions, achieving an accuracy of 93.4% and an average Macro-F1 of 91.2%. This method significantly outperforms baseline models such as Support Vector Machine (SVM), Text Convolutional Neural Network (TextCNN), and Long Short-Term Memory (LSTM), and demonstrates particularly notable advantages in identifying negative reviews with a recall of 89.4%. This study thus verifies the effectiveness of multi-module collaborative modeling in sentiment analysis for vertical domains and offers a practical technical path for public opinion monitoring and service optimization in smart cultural tourism.