Traditional lower limb prosthesis motion intent recognition methods often use multimodal sensor signals, which bring certain complexity and lags of pattern transition recognition. This paper proposes a datadriven based intelligent lower limb prosthesis motion intent recognition method. After redefining the movement patterns of unilateral lower limb amputees, only the inertial sensorsare used to collect the time series data in the swing phases of the healthy side. The Gaussian mixture modelhidden Markov model (GMMHMM) is selected as the classifier to recognize the motion intent of lower limb prosthesis. The experiment results show that the recognition rate of the method reaches 9899% in steady patterns: levelground walking, ramp ascent, ramp descent, stair ascent and stair descent, and 9692% in 13 motion patterns that contain 5 steady patterns and 8 transition patterns. The method proposed in this paper can greatly improve the recognition performance of lower limb prosthesis motion intent, and help the unilateral lower limb amputees to walk naturally, smoothly and steadily.