The dung beetle optimization (DBO) algorithm is a novel heuristic algorithm inspired by the behaviors of dung beetles. It exhibits faster convergence speed and stronger ability to escape local optima compared to other algorithms. However, the DBO algorithm lacks the capability of performing feature selection. In this paper, propose algorithm of dung beetle and grey wolf fusion (DBOG) as an improvement to the DBO algorithm specifically designed for feature selection tasks. The DBOG incorporates three enhancement strategies: elite initialization population strategy, grey wolf-dung beetle fusion strategy, and runtime acceleration strategy. These strategies aim to further enhance the performance of the DBO algorithm in feature selection tasks. Additionally, we provide pseudocode for the overall algorithm. Experimental results demonstrate that, compared to other improved heuristic algorithms, the DBOG achieves higher accuracy and lower-dimensional feature subsets across 12 classification datasets. Moreover, it offers advantages such as faster convergence speed and computational efficiency.