Recommending travel routes for tourists has become a research hotspot in the present tourism industry. Various machine learning methods have been well studied for recommendation systems. However, the accuracy of these systems is not up to the expected levels. In order to increase the ranking accuracy, as well as, the recall rate of the recommended routes by the personalized travel recommendation algorithm, in this paper, a fuzzy consistent matrix technique is introduced for personalized travel recommendation systems. First of all, the user’s travel interest data are obtained through the fuzzy consistency matrix. Next, the user’s travel interest data are de-noised to determine the user’s preferred interest points and the coverage of the user’s interest points. Besides this, the constraints of the route recommendation systems have also become a research topic. Therefore, the proposed method also uses the fuzzy consistent matrix technology to determine the users’ similarity and classifies the personalized recommendation algorithm. Finally, a personalized travel recommendation model is constructed to comprehend the personalized travel route recommendation. The investigational outcomes, using real datasets, illustrate that when the number of routes is 5000, then the accuracy rate of the proposed method is approximately 97.21%, and the accuracy rate of the recommendation ranking is 97.54%. We also observed that when using 60 GB of data, the recall rate of the proposed method, for personalized travel recommendation routes, can reach as high as 97.59%.