Gait analysis and evaluation are vital for disease diagnosis and rehabilitation. Current gait analysis technologies require wearable devices or high-resolution vision systems within a limited usage space. To facilitate gait analysis and quantitative walking-ability evaluation in daily environments without using wearable devices, a mobile gait analysis and evaluation system is proposed based on a cane robot. Two laser range finders (LRFs) are mounted to obtain the leg motion data. An effective high-dimensional Takagi-Sugeno-Kang (HTSK) fuzzy system, which is suitable for high-dimensional data by solving the saturation problem caused by softmax function in defuzzification, is proposed to recognize the walking states using only the motion data acquired from LRFs. The gait spatial-temporal parameters are then extracted based on the gait cycle segmented by different walking states. Besides, a quantitative walking-ability evaluation index is proposed in terms of the conventional Tinetti scale. The plantar pressure sensing system records the walking states to label training data sets. Experiments were conducted with seven healthy subjects and four patients. Compared with five classical classification algorithms, the proposed method achieves the average accuracy rate of 96.57%, which is improved more than 10%, compared with conventional Takagi-Sugeno-Kang (TSK) fuzzy system. Compared with the gait parameters extracted by the motion capture system OptiTrack, the average errors of step length and gait cycle are only 0.02 m and 1.23 s, respectively. The comparison between the evaluation results of the robot system and the scores given by the physician also validates that the proposed method can effectively evaluate the walking ability.