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Currently, the main obstacle hindering the development of gait recognition systems is the influence of changes of clothing, carrying goods and viewpoint on the gait profile of pedestrians. In this paper, we propose a gait recognition method based on Gravity Center Tracks (GCT) that converts the original video of the gait into a binary image sequence, calculates the coordinates of the gravity center in each frame of image, sequentially connects the coordinates of the gravity centers of all frames in the image sequence to obtain the GCT. The GCT is preprocessed and spectrum analyzed, and the transformed DFT coefficients are input into a K cluster and BP neural network for recognition after normalization. The test shows that the method is capable of achieving excellent recognition under conditions with different clothing and carrying goods and still maintains a high recognition rate without retraining models when the pedestrians change walking direction and walking conditions.