Magnetic resonance imaging (MRI) is extensively utilized in psychiatric disorder diagnosis. However, the high-dimensional nature of the data poses challenges in network training. We propose a classification approach leveraging autoencoder and multilayer perceptron techniques. Initially, a convolutional autoencoder is designed to reduce the dimensionality and extract features from the input feature map using its encoder component. Subsequently, the extracted features are fed into the designed multilayer perceptron for training and testing, enabling joint classification based on dual features. Finally, we apply this method to classify attention deficit hyperactivity disorder (ADHD), and the experimental results demonstrate the effectiveness of our approach in significantly enhancing the accuracy of ADHD classification.