According to recent research, plantar pressure can reflect various characteristics of the human body, which is promising for biometric identification. In our study, the feasibility and methodology of biometric identification by plantar pressure is discussed. Insoles with eight pressure sensors are used to collect over 14 000 steps of 14 participant as database. After that, the scientificity of classification is discussed by unsupervised learning, and the influence of ground conditions on pressure data is discussed. Convolutional neural network (CNN) is used as the classifier to evaluate the classification performance, and the effects of gait segmentation and multiple gait cycles on improving the accuracy are studied. Experimental results show that the accuracy of data classification after segmentation is 98. 8% , while that without segmentation is 93. 6% . When using 3 and 5 gait cycles for classification, the accuracy rise up to 99. 4% and 99. 8% . The results suggest that CNN with segmented data and selecting multiple gait cycles for classification has practical value in biometric identification utilizing plantar pressure.