In recent years, due to the fundamental role played by the central nervous system in emotion expression, electroencephalogram (EEG) signals have emerged as the most robust signals for use in emotion recognition and inference. Current emotion recognition methods mainly employ deep learning technology to learn the spatial or temporal representation of each channel, then obtain complementary information from different EEG channels by adopting a multi-modal fusion strategy. However, emotional expression is usually accompanied by the dynamic spatio-temporal evolution of functional connections in the brain. Therefore, the effective learning of more robust long-term dynamic representations for the brain's functional connection networks is a key to improving the EEG-based emotion recognition system. To address these issues, we propose a brain network representation learning method that employs self-attention dynamic graph neural networks to obtain the spatial structure information and temporal evolution characteristics of brain networks. Experimental results on the AMIGOS dataset show that the proposed method is superior to the state-of-the-art methods.