In order to delve into the relationships of information flow among various brain regions within causal brain networks，a causal network emotion recognition methodology grounded in electroencephalogram（EEG）signals across communities is proposed.Firstly，time-frequency domain features are extracted from the preprocessed EEG signals.The partial directed coherence（PDC）method is adopted to build the casual brain network，and the Infomap community detection algorithm is used to divide the communities of the brain network.Next，a graph representation of the brain network is formulated，in which the causal interactions，quantified by PDC values between different communities，serve as the edge features，while the node features are defined by the weighted average differential entropy computed for each respective community. Finally，this constructed graph data is fed into a graph convolutional neural network for the ultimate task of emotion classification and recognition.Experimental results demonstrate that compared with the conventional full-channel causal emotion recognition approaches，the proposed method decreases the computational complexity by leveraging the directed causal information between the brain sections，and successfully maintains a high level of emotion recognition accuracy.