Students’ learning situation is closely related to their classroom attention state. In order to explore whether attention training can improve classroom attention， the electroencephalogram （EEG） signals of non-attention and attention states of ten students before and after α music training are collected and compared. It is worth noting that EEG signal is dynamic in nature and has the characteristics of low signal-to-noise ratio and high redundancy. In order to avoid the problem of poor recognition of EEG signals directly through neural network， 11 features of signal sample entropy （SampEn）， energy and energy ratio of each band are extracted， and these features are fused into multi-feature images as the input of neural network model. In addition， the weighted fusion of AlexNet and VGG11 network models is used to form a double convolution neural network （CNN）， which can further improve the performance of image classification. The results show that the performance of the fusion model with double CNN can achieve a better performance compared with the model with single CNN. In particular， the recognition accuracy of the proposed model can reach 97.53%. It can be found that after α music training， the EEG features of the subjects are significantly different from those before， and the classification accuracy of the network model can be 4% higher than that before training. This observations show that the considered α music training can improve the attention level of healthy students.