To address the problem of low recommendation quality and low recommendation efficiency of current collaborative filtering recommendation algorithms, we propose a collaborative filtering recommendation algorithm based on an improved bee colony K-means clustering model. Firstly, based on user attribute information, the algorithm uses the improved bee colony K-means algorithm to cluster users and establish a user clustering model. Secondly, we calculate the distance between target users and the clustering center in the user clustering model, and the cluster with the minimal distance is taken as the retrieval space of active users. Finally, we search the nearest neighbor of the target user by the similarity calculation according to the useritem scoring matrix, and generate a recommendation list via the information of the nearest neighbor. Experimental results show that the proposed algorithm can achieve lower MAE and shorter running time, and it can enhance the quality and efficiency of recommendation.