By combining the method of questionnaire survey and automatic crawling, a lot of useful tourist information such as users’ personal information, users’ ratings of tourist attractions and other tourism data are obtained. Based on the crawled tourism data, a hierarchical sampling method is applied in turn to generate the “Smart Travel” dataset which contains the important demographic information. Then a user clustering?based collaborative filtering algorithm is implemented to compute the semantic similarity between target user and each clustering center after the users’ ratings of tourist attractions in the “Smart Travel” dataset is preprocessed. Finally, a hybrid recommendation list is generated by absorbing the demographic information obtained by the hierarchical sampling model. Experimental results show that compared with the traditional method, two evaluating indicators like the root mean square error (RMSE) and the mean absolute error (MAE) of the presented algorithm reduce 11.5%—64.9% and 18.8%—47.7%， respectively. Meanwhile, compared with the main baselines, the recommendation precision gets a large improvements as well as the recall rate and better recommendation results are obtained ultimately.