Community detection is a useful tool for mining hidden information in social networks. And Label Propagation Algorithm (LPA) is a common algorithm in the community detection algorithm, which does not require any prior knowledge and runs fast. Aiming at the problem of the instability of community detection algorithm results caused by the strong randomness of label propagation algorithm, an improved Label Propagation Algorithm based on Random Walk (LPARW) was proposed. Firstly, according to the random walk on the network, the importance order of nodes was determined, so as to obtain the update order of nodes. Secondly, the update sequence of nodes was traversed, and the similarity calculation between each node and the node before it was performed. If the node and the node before it were neighbor nodes and the similarity between them was greater than the threshold, then the node before it was selected as the seed node. Finally, the label of the seed node was propagated to the rest of the nodes in order to obtain the final division result of the communities. The proposed algorithm was comparatively analyzed with some classic label propagation algorithms on 4 labeled networks and 5 unlabeled real networks. Experimental results show that the proposed algorithm is better than other comparison algorithms on classic evaluation indicators such as Normalized Mutual Information (NMI), Adjusted Rand Index (ARI) and modularity. It can be seen that the proposed algorithm has the good community division effect.