In view of the current emotional electroencephalogram (EM-EEG) identification research on time scales is difficult to grasp the time domain information and the spatial domain information is easy to ignore the recognition rate is stagnant, and collect the EM-EEG with too many channels in the excessive information redundancy and increasing cost of information processing problems, it puts forward the space-time convolution based on CNN optimization study on EM-EEG identification fusion network. The fusion network is composed of a parallel long convolution (L-Conv) CNN that extracts EM-EEG time domain information and a CNN that extracts EM-EEG spatial information. Particle swarm optimization (PSO) is used in the time-space optimization of the CNN model. The L-Conv scale in CNN has been optimized, and use the short time power spectrum ( STPS) correlation analysis method of the spatial CNN channel number optimization model, temporal and spatial domain features in EEG are extracted deeply and effectively. The results show that the proposed optimization of space-time convolution integration CNN on SEED IV data set for peace, sadness, fear, happy four final accuracy can reach 90. 13%, compared with the traditional single CNN recognition accuracy rate increased by 4. 76%, and channel number from 62 to 33 road, shrank by 46. 77%, confirmed the feasibility of this method.