Aiming at the problem that it is difficult to fully extract deeper features using single-scale convolution and traditional ReLU activation function when using deep learning models to identify EEG signals of adolescent schizophrenia. Put forward a kind of multi-scale convolutional neural network model with adaptive ReLU(MSAPNet) for adolescent patients with schizophrenia and healthy adolescent brain electrical signal classification. Firstly, a multi-scale cascade module is used to extract the input 3D feature matrix containing the original EEG spatial information. Secondly, the features at different levels were fused through the designed feature fusion module. Multi-scale down sampling module is then used to decrease the dimension of feature maps. Finally, using the classification module to complete identification and detection of disease. The experimental results show that the MSAPNet of disease identification accuracy, sensitivity, specificity and accurate rates and F1 score can be achieved respectively 97.21%, 97.51%, 96.86%, 97.29% and 97.40%, compared with the related research has better detection performance, proved the effectiveness of the proposed method.