Abstract: The decoding analysis of gait features based on electroencephalogram (EEG) and the reliable recognition and prediction of motion intention are the core problems of brain-computer interface (BCI) based human-machine hybrid rehabilitation training system and intelligent walking robot. In order to realize the recognition of the most basic gait processes such as standing, sitting and resting states, this study proposes a feature representation method based on multi-layer functional brain network using EEG. Combined with the statistical analysis of various network features, these parameters sensitive to different movements are determined, and support vector machine, linear discriminant analysis, logistic regression and naive bayes algorithms are applied to complete the classification of different actions. Experiment results show the proposed method can realize the recognition of the three actions, and the recognition accuracy of standing, sitting and resting state is higher than 71% and the highest accuracy is 77% for 13 subjects. Multi-layer brain network analysis shows the motion action of lower limb can weaken the interdependence between brain regions, resulting the sparsification of the topology structure. This study has certain reference value for understanding the changes of brain cognitive process during lower limb movement, carrying out BCI based rehabilitation strategies, and developing corresponding rehabilitation systems.