Abstract: Detecting community structure of directed networks is of significance for understanding the structures and functions of complex systems. In this paper, we develop a spectral algorithm using multiple eigenvectors of the Laplacian matrix (MEL) in directed networks, where the c eigenvectors of the smallest eigenvalues of the Laplacian matrix are taken into account. We compare with the spectral optimization method (SOM) and simulated annealing (SA) algorithm of modularity matrix in directed networks on synthetic and empirical networks. The experimental results indicate that, the values of the normalized mutual information (NMI) obtained by our algorithm are approximated 1 when the community structures are clearly. The proposed algorithm outperforms the SOM and SA algorithms when the community structures are not clearly. In addition, the numerical results for empirical data set show that the modularity values Q could be enhanced by 17.28% and 19.21% respectively. This work may be helpful to analyze the relationship between the properties of Laplacian matrix and community structures in directed networks.