Attention deficit hyperactivity disorder （ADHD） seriously affects children’s development， so extensive attention has been paid to its effective diagnosis. A new method for calculating graph similarity is proposed， which combines the topological information of brain networks with signals on the network. The Pearson correlation coefficient is used to construct the fully connected brain network. Based on the sparse representation， the node subnetwork is extracted from the underlying structure， and the similarity of the subnetwork is calculated according to the graph kernel function. Finally， the global index of brain network similarity is given. Experimental results of classifying ADHD-200 in the public dataset characterized by similarity between subjects show that the proposed method can distinguish ADHD patients and healthy people with 93.1% accuracy， and the classification performance is significantly superior than other existing methods. In addition， it is found that ADHD patients have stronger connections in brain regions， such as anterior central gyrus， thalamus， hippocampus and insula.