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• We propose a community detection algorithm based on a deep sparse autoencoder. • We combine the path weight matrix with the weighted adjacent paths of the node to obtain the similarity matrix. • The feature matrix has stronger ability to express the features of the network. • The proposed algorithm can more accurately identify community structures. At present, community detection methods are mostly focused on the investigation at unweighted networks. However, real-world networks are always complex, and unweighted networks are not sufficient to reflect the connections among real-world objects. Hence, this paper proposes a community detection algorithm based on a deep sparse autoencoder. First, the second-order neighbors of the nodes are identified, and we can obtain the path weight matrix for the second-order neighbors of the node. We combine the path weight matrix with the weighted adjacent paths of the node to obtain the similarity matrix, which can not only reflect the similarity relationships among connected nodes in the network topology but also the similarity relationships among nodes and second-order neighbors. Then, based on the unsupervised deep learning method, the feature matrix which has a stronger ability to express the features of the network can be obtained by constructing a deep sparse autoencoder. Finally, the K-means algorithm is adopted to cluster the low-dimensional feature matrix and obtain the community structure. The experimental results indicate that compared with 4 typical community detection algorithms, the algorithm proposed here can more accurately identify community structures. Additionally, the results of parameter experiments show that compared with the community structure found by the K-means algorithm, which directly uses the high-dimensional adjacency matrix, the community structure detected by the WCD algorithm in this paper is more accurate. [ABSTRACT FROM AUTHOR]