In order to solve the shortcomings of the existing mathematical expressions of the user's personalized recommendation search and to increase the accuracy of the user's personalized information recommendation, in this study, the collaborative filtering algorithm, the related theory of fuzzy sets, and the evaluation methods were introduced at first. Then, the establishment of collaborative filtering model and working principle, user similarity measure, generation of mathematical expression recommendation list, design of mathematical expression recommendation model and implementation of function were introduced. Finally, the application of the collaborative filtering algorithm in the user's personalized information recommendation mathematical expression was verified by collecting data. The results showed that the recommendation mathematical expression model based on collaborative filtering algorithm had higher accuracy in collecting user personalized information, and the accuracy of the system was higher than other recommendation algorithms, which can better meet the individual needs of users. This research can provide a fast and convenient way for users to search for personalized information, and has a good guiding significance for the retrieval and recommendation of informational scientific and technical literature.