Community detection is very important in theoretical and practical for complex research. According to the principle of distributed word vector, a community detection algorithm based on node embedding vector (CDNEV) is proposed in this study. In order to construct the distributed vector of network nodes, a heuristic random walk model is put forward. The node sequence obtained by the heuristic random walk model is used as the context for nodes, and the distributed vector of nodes is learned by SkipGram model. Based on the distributed vector of nodes that are selected from the local node as the center of the K-Means clustering algorithm center, all nodes in a network are clustered with K-Means algorithm, and the community structure are conclude by clustering result. Based on real complex networks and artificial networks used in other state-of-the-art algorithms, comprehensive experiments are conducted. For comparison purpose, typical community detection algorithms are selected to be evaluated. On real networks, the F1 value of CDNEV algorithm is increased 19% on average. The F1 value can be increased by 15% on artificial networks. Experimental results demonstrate that both accuracy and efficiency of CDNEV algorithm outperform other state-of-the-art algorithms.