As a hot issue of current research, tourism information service has higher and higher requirements for intelligent construction. Tourism service recommendation is the embodiment of smart tourism. However, there are still obvious deficiencies in solving the problem of Internet information load and improving user experience. Through functional analysis, architecture design, selection of relevant development frameworks, and improvement of collaborative filtering algorithms, a stable, reliable, high-performance, multi-functional intelligent travel recommendation system can be developed that can complete personalized recommendations. It can achieve the purpose of improving the efficiency and accuracy of recommendation and recommending tourism-related information to users in a targeted manner. Analyze the test plan and test the recommended algorithm module. In the case of different concurrency and database levels, the system response time is 0.9 s and 1 s, respectively. And in the case of high throughput, the system response time is 1.5 s, indicating that the system is running stably. It not only tested the storage and calculation of big data but also improved the usability of the travel information service recommendation system and the user experience of the system.