Disclosed is a natural movement electroencephalogram (EEG) recognition method based on source localization and a brain network, which includes the following steps: (1) performing multi-channel EEG measurement for natural movements; (2) preprocessing acquired EEG signals, and extracting the movement-related cortical potential (MRCP), and θ, α, β, and γ rhythms; (3) determining a lead field matrix of the signals, calculating initial solutions of sources by means of L1 regularization constraint, and then performing iteration by means of successive over-relaxation to obtain a source localization result; (4) by using the sources as nodes, calculating PLV between each pair of sources at each time point by means of short-time sliding window, and establishing brain networks; and (5) calculating a network adjacency matrix at each time point and five brain network indicators, introducing these features into a classifier for training and testing, and conducting a statistical test for the brain network indicators. The present disclosure makes improvements to the conventional source localization method by using the T-wMNE algorithm in combination with successive over-relaxation, and establishes brain networks by using the sources as nodes, thus improving the EEG decoding accuracy for natural movements and revealing the neural mechanism of the human body.