About a third of the world’s population suffers from insomnia， and many studies have shown that elevating high frequency band activity is an important cause of insomnia. However， due to the existence of large disturbance factors， it is difficult to evaluate in daily resting state conditions. Therefore， the Beta and Gamma bands of electroencephalogram （EEG） are extracted from patients with primary insomnia and normal controls. The phase locking value （PLV）， which is more suitable for nonlinear and non-stationary signals such as EEG， is used to obtain the adjacency matrix to construct rest-state functional brain network. The adaptive threshold technology is used to binarize the adjacency matrix. In order to fuse various characteristics of brain networks， a comprehensive measurement index of brain networks is proposed for insomnia detection. In Beta frequency band， the comprehensive indexes are significantly different between the primary insomnia group and the normal control group （p=0.044）. The automatic classification using support vector machine （SVM） achieves the accuracy of 77.7% and the sensitivity of 90.7% in Beta band. Compared with the original network characteristics， the classification accuracy and the sensitivity of the proposed comprehensive index are increased by 9.4% and 20.7%， respectively. At the same time， compared with the existing studies， the classification accuracy and the sensitivity of the proposed comprehensive index are increased by 19.4% and 20.7%， respectively. It shows the proposed method has potential application value in the diagnosis of insomnia.