With the development of domestic economy and the improvement of people’s living standard, tourism has become more and more popular as a leisure lifestyle. The explosive growth of the mobile Internet has caused the problem of “information overload”. The travel recommendation system can help tourists obtain the travel information that users are interested in from the massive data. Ecological health tourism is a special tourism product with ecological environment as the background and leisure health activities as the theme. With the development of China’s urbanization and the intensification of population aging, the Chinese people’s demand for health tourism products and ecological health tourism market is becoming stronger and stronger, and the development prospect is extremely broad, but there is not much research in this field in the academic circles at present. This paper applies the Collaborative Filtering (CF) to travel recommendation to provide users with accurate travel recommendation services. However, because the traditional CF only relies on a single user’s rating data, and has its own defects, it cannot meet the complex needs of users in the tourism industry. This paper improves the traditional CF and designs and implements a tourism recommendation system on this basis. Combine Spark cloud computing platform technology and TC-Personal Rank algorithm to achieve a breakthrough in the algorithm. Through experiments, it can be found that the accuracy of product recommendation can be improved by 75.3% for the algorithm designed in this paper. Overall, the recall rate can reach 65.7%. And it can also achieve good results in recommendation satisfaction and recommendation coverage.