Aiming to accurately identify the fall and draganddrop abnormal gait during assisted walking of the patients with lower limb dysfunction, a noncontact abnormal gait recognition method based on nodeiteration type fuzzy Petri Net is proposed from the universality and convenience of various user groups. Firstly, the structure of the rehabilitation training robot is discussed, and the behavior characteristics of the abnormal gait of fall and draganddrop often occurring in the course of assistant walking are described. A multichannel proximity sensor array is developed to detect gait information in real time. Integrating the intention vector of walking direction, the gait deviation, frequency and body incline angle are taken as the input parameters of the detection system. Based on the fuzzy membership function, the network ignition mechanism is generated, and the nodeiteration type fuzzy Petri Net is established to recognize the abnormal gait. Finally, an abnormal gait recognition method based on nodeiteration fuzzy type Petri net is proposed, and abnormal gait operator reasoning experiment and multimode walking fall detection experiment of walking rehabilitation training robot are carried out. The experiment results show that the algorithm can accurately recognize the abnormal gait of different walking habit groups using walking rehabilitation robot, improve the safety and comfort of the assisted walking of the users, and the recognition rate of abnormal gait reaches to 912%. This proposed method can be used in the daily living and rehabilitation training of the users with lower limb dysfunction using similar walking aids.