The internal community structure is evolving with the change of network structure. These changes in different time slices can be defined as four different evolutionary events: survive

split

fusion and disappearance. In this paper

the network representation learning method is used to map the graph embedding of the network into the low-dimensional vector space to study the prediction of dynamic community evolution events. In the features

based on the features of community internal attributes

the change of temporal attributes

and the previous community evolution events

the potential structure characteristics of the four evolutionary events are introduced and obtained by using random walk and Softmax. In the model

the strategy of deep random forest is proposed. Feature fusion and feature training are carried out by using the attention mechanism and Monte Carlo feature sampling strategy

which overcomes the shortcomings of the existing algorithms that only acquire local structural features. Finally

by comparing SVM

XGBOOST

RIDGE model training in the DBLP

FACEBOOK and Bitcoin datasets

it is confirmed that the embedding feature of community structure and the attention deep random forest model improvement have greatly improved the accuracy of final prediction.