The invention relates to a dynamic
community discovery method based on a recurrent
convolutional neural network and an auto-
encoder. The method comprises the following steps: firstly, constructing a network spatial
feature learning model based on a
convolutional neural network, and learning spatial topological features of the network to obtain a network spatial
feature vector; secondly, fusing a network spatial
feature learning model based on a
convolutional neural network, constructing a network spatial-temporal
feature learning model based on a
recurrent neural network, the convolutional neural network and an auto-
encoder by taking a network spatial
feature vector as an input of the model, and learning spatial-temporal features of the network to obtain a network spatial-temporal featurevector; and finally,
community discovery is performed on the basis of the network space-time
feature vector so as to detect the dynamic
community structure of the
social network. The method can be applied to analyzing the
social network, autonomously learning and extracting the spatial and temporal features of the
social network, and can further improve the
modularity of a
community structure, thereby revealing the topological structure and the like of a real network, and further effectively predicting network user behaviors,
information propagation and the like.