With the continuous improvement of people’s living standards, tourism has also become one of the important activities for people to improve their quality of life. Especially in the context of the digital economy, the development of the tourism industry has been promoted very rapidly. However, most of the current tourism information recommendation systems are information recommendation in a fixed mode, and will not make corresponding changes according to changes in the actual situation. In order to solve this problem, this paper analyzes the development theory of tourism information system, and proposes the design and implementation scheme of tourism information recommendation system based on machine learning. The tourism information recommendation system mainly provides services for tourists and tourism industry players. Its main functions include map navigation, traffic query data and intelligent guidance. Aiming to further study the function of the tourism information recommendation system designed in this paper in real life, it conducts a test research on the recommendation system. The test results show that the tourism information recommendation system designed in this paper based on machine learning has certain practical significance. It can increase tourist attraction revenue by 4.7% and reduce traffic congestion by 3.3%. This prevents tourists from entering the peak tourist period, resulting in a lower feedback rate.
