As sensitivity and specificity of current microRNA identification methods are not ideal or imbalanced because of emphasizing new features but ignoring weak classification ability and redundancy of features. An ensemble algorithm based on feature clustering and random subspace method was proposed, named CLUSTER-RS. After eliminating some features with weak classification ability using information ratio, the algorithm utilized information entropy to measure feature relevance and grouped the features into clusters. Then it selected the same number of features randomly from each cluster to compose a feature set, which was used to train base classifiers for constituting the final identification model. By tuning parameter and selecting base classifiers to optimize the algorithm, experimental comparison of CLUSTER-RS and five classic microRNA identification methods (Triplet-SVM,miPred,MiPred,microPred,HuntMi) was conducted using latest microRNA dataset. CLUSTER-RS was only inferior to microPred in sensitivity and performed best in specificity, and also had advantage in accuracy and Matthew correlation coefficient. Experiments show that, CLUSTER-RS algorithm achieves good performance and is superior to the rivals in the aspect of balance between sensitivity and specificity.