At present, the clinical diagnosis of depression is mainly based on doctors′ experience and patients′ subjective feeling, which is highly subjective, low accuracy and time-consuming. With the development of neuron electrophysiology and computer technology, the objective classification and recognition of depression become possible. However, the existing research methods for the classification and identification of depression based on resting-state EEG signals are relatively simple, and it is necessary to further explore accurate, comprehensive and effective EEG features. In this article, a single-channel resting-state EEG depression classification and recognition method based on Higuchi′s Fractality Dimension (HFD) and Lempel-Ziv Complexity (LZC) is proposed based on the design of two experimental modes to obtain higher classification accuracy with fewer features. First, the resting-state EEG signals of 8 major depression disorders and 8 healthy control subjects are collected. Then, their nonlinear dynamic feature parameters HFD and LZC are extracted. Finally, the feature data are input into a nonlinear support vector machine model for classification recognition. Results show that the sensitivity, specificity and classification accuracy obtained by the combined feature are the highest at 98. 12% , 96. 67% and 95. 10% , respectively, which are 23. 05% , 17. 02% and 19. 29% higher than independent HFD/ LZC. Meanwhile, the main part of the model only takes about 12 s. The findings have important implications for the identification and auxiliary diagnosis of depression in clinical practice.