Inertial Measurement Unit (IMU) is widely used in the acquisition and control of human motion information due to its small size, low costs, high accuracy, and strong timeliness. However, it still has obvious limitations in the time-series feature extraction and the data about gait environment during gait recognition. Aiming at the complexity and poor applicability of lower-limb gait recognition based on feature extraction, this study proposes a new method of human gait recognition based on Tsfresh-RF feature extraction. Firstly, an algorithm of human gait recognition based on Tsfresh time-series feature extraction and Random Forest (RF) is constructed by a human gait data set acquired by IMU. Secondly, experiments including nine gaits are carried out by this algorithm on different sensor positions, such as climbing, walking, and turning. Finally, the average classification accuracy of the proposed method reaches 91.0%, which is significantly higher than that of traditional Support Vector Machine (SVM) and Naive Bayes (NB) methods. In addition, the proposed algorithm is robust, which will provide a favorable basis for subsequent control of lower-limb exoskeleton robots.