Research on AI Tool Development System for Improving the Efficiency of Intelligent Services in Tourism Cities

This paper proposes an intelligent tourism recommendation system based on artificial intelligence (AI) tools, aiming to provide tourists with more accurate personalized recommendation services. The system adopts the Wide Deep model architecture and combines natural language processing (NLP) technology to achieve multi-dimensional user behavior analysis and recommendation optimization. In the Wide part, the system captures the basic preference characteristics of tourists, such as historical browsing records, geographic location, and basic user information, which helps to quickly match tourist resources related to tourists' preferences. The Deep part uses deep learning technology to deeply explore tourists' potential needs and behavior patterns. Through the analysis of complex interactive behaviors, the system can better understand users' personalized preferences. In addition, the system also integrates natural language processing technology to further enrich the input data of the system by analyzing tourists' feedback, comments, and public information on social media, thereby improving the comprehensiveness and accuracy of recommendations. This paper designs an efficient algorithm process to ensure high computational efficiency when processing large-scale data. Simulation tests show that the system improves the recommendation accuracy by 12% compared with traditional recommendation methods, and shortens the response time by 20%, which fully verifies the advantages of the system in practical applications.