Louvain Method (LM) is an algorithm to detect community in complex network based on modularity optimization. Since there is no method to calculate the gain of modularity after nodes leave their community in the existing research, a method was presented to calculate the modularity-gain after nodes leave their community based on the definition of modularity and the method for calculating the modularity-gain after nodes merge. Secondly, aiming at the problem that LM requires large memory space, an improved algorithm was proposed with the strategy of separating isolated nodes. In each iteration of the algorithm, isolated nodes of the input network were separated in advance, only the connected nodes of the input network can actually participate in the iterative process. Isolated nodes and non-isolated nodes were stored respectively when storing communities detected. The experimental results based on real networks showed that the requirement of memory space was reduced by more than 40% in the improved algorithm, and the running time of the algorithm was further reduced. Experimental results indicate that the improved algorithm has more advantages in dealing with real networks.