Traditional clustering-based band selection methods mostly belong to hard clustering, which are not accurate enough to divide the bands. To solve this problem, this paper proposes an unsupervised band selection method based on fuzzy C-means clustering (FCM). By introducing the firefly algorithm (FA), an FCM-FA is obtained, after which the global search feature of FA is used to solve the problem for which the FCM only obtains a locally optimal solution in certain circumstances. Classification experiments on two public hyperspectral datasets show that the proposed FCM-FA achieves the classification accuracy for all bands in 55.9% of 136 experiments, the optimal classification accuracy is achieved in 77.9% of the cases, the introduction of FA effectively improves the effect of FCM, with the overall accuracy increasing by 3.12 percentage points, and Kappa is increased by 4.26 percentage points at most. Hence, our results verify that FCM-FA can significantly reduce the amount of data while retaining the main information of original data that can be further promoted and studied.