The classical artificial bee colony (ABC) algorithm is also faced with slow convergence speed, and it is easy to fall into local optimality, so there are still many problems in feature selection based on this algorithm. Therefore, a feature selection method based on the rough entropy of granularity and an improved bee colony algorithm, namely FS_GREIABC, is proposed. Firstly, a new information entropy model, namely the rough entropy of granularity, is proposed by combining the knowledge granularity and the rough entropy in the rough set. Secondly, the rough entropy of granularity is applied to the ABC algorithm, and a fitness function based on the rough entropy of granularity is proposed, so as to obtain a new fitness calculation strategy. Thirdly, in order to improve the local search ability of the ABC algorithm, a cloud model is introduced into the following bee stage. Experiments on multiple UCI datasets and software defect prediction datasets show that FS_GREIABC not only selects fewer features but also has better classification performance than the existing feature selection algorithms.