In this paper, a collaborative filtering recommendation algorithm based on user’s local differential privacy is investigated. First, user-based collaborative filtering recommendation experiments are conducted using different similarity calculation methods. Meanwhile, the similarity calculation method with the best effect of Pearson coefficient is used as the basis for the subsequent experiments. Moreover, on the basis of the total privacy budget remaining unchanged, the impact of different privacy budget allocation mechanisms on the recommendation performance is explored. The method of dynamically allocating privacy budget based on the number of user ratings is innovatively proposed, and accuracy and recall are selected as the evaluation indexes. A series of comparative experiments are carried out on the public dataset, and the experimental results show that the accuracy and recall are improved compared with the Laplace noise mechanism; finally, the impact of the change of the total privacy budget on the experimental effect is explored.