The recommendation system predicts the unknown information according to the user's historical information. Sparsity of user item scoring matrix is one of the main bottlenecks faced by recommendation system. Cross-domain recommendation system is an effective method to solve the problem of data sparsity. In this study, an Efficient Recommendation Algorithm based on effective Feature Subset selection (FSERA) is proposed. FSERA extracts subset information of auxiliary domain to expand target domain data, so as to collaboratively filter recommendation for target domain. In this study, K-means clustering algorithm is used to extract data from the auxiliary domain to reduce redundancy and noise, and to obtain an effective subset of the auxiliary domain, which not only reduces the complexity of the algorithm, but also expands the target domain data and improves the recommendation accuracy. Experiments show that this method has higher recommendation accuracy than traditional methods.