To solve the problems of the mutual interference of multi-channel ElectroEncephaloGraphy （EEG）， the different classification results caused by individual differences， and the low recognition rate of single domain features， a method of channel selection and feature fusion was proposed. Firstly， the acquired EEG was preprocessed， and the important channels were selected by using Gradient Boosting Decision Tree （GBDT）. Secondly， the Generalized Predictive Control （GPC） model was used to construct the prediction signals of important channels and distinguish the subtle differences among multi-dimensional correlation signals， then the SE-TCNTA （Squeeze and Excitation block-Temporal Convolutional Network-Temporal Attention） model was used to extract temporal features between different frames. Thirdly， the Pearson correlation coefficient was used to calculate the relationship between channels， the frequency domain features of EEG and the control values of prediction signals were extracted as inputs， the spatial graph structure was established， and the Graph Convolutional Network （GCN） was used to extract the features of frequency domain and spatial domain. Finally， the above two features were input to the fully connected layer for feature fusion in order to realize the classification of EEG. Experimental results on public dataset BCICIV_2a show that in the case of channel selection， compared with the first EEG-inception model for ERP detection and DSCNN （Shallow Double-branch Convolutional Neural Network） model that also uses double branch feature extraction， the proposed method has the classification accuracy increased by 1.47% and 1.69% respectively， and has the Kappa value increased by 1.25% and 2.53% respectively. The proposed method can improve the classification accuracy of EEG and reduce the influence of redundant data on feature extraction， so it is more suitable for Brain-Computer Interface （BCI） systems.