The bird gait score (GS) is an important tool for evaluating the gait status of broiler. GS0~GS5 corresponds to the broilers whose limping level varies from low to high. The level of limping is used as an important indicator to measure the health of broilers. At present, traditional methods for gait assessment of broiler are mostly completed by visual inspection. The process is timeconsuming with low standardization. The dynamic feature variables extracted from video were used to evaluate the gait status of broilers based on decisiontree, and a fast, stable and noncontact broiler gait evaluation method was explored. The experiment was conducted at Quanjiao Broiler Breeding Center of Wenzhou Group, from December 2017 to January 2018. A total of 260 broilers (GS0~GS4) were selected. Each broiler was subjected to twice walkingexperiments. The experiment was conducted in a special broiler walkway. Two cameras were placed on the opposite side of the walkway and at the top of walkway, and videos were collected horizontally and vertically. Each frame of the video underwent image reorganization, filtering for pretreatment in HSV space. The broiler projection area was calculated by the least squares ellipse fitting based on vertical image, and the dynamic parameters such as the walking speed, stridelength, stridedifference value, and walking steps of the broilers were calculated based on horizontal image. Based on the study of the dynamic parameters of GS0 broilers, the linear fitting relationship between walking speed, stridelength and projection area of broilers was obtained by the least square method, the coefficient of certainty was 0.8051 and 0.7935, respectively. According to the fitting results, based on the different top projection areas of the broiler, the ideal stride and ideal speed of the broiler were proposed. Then, according to the difference between the actual value and the ideal value of the parameters such as stride and speed, the abnormal index of dynamic parameters in broiler walking was defined. Taking the anomaly index, including speed, stride and step difference as training attributes, the C45 decision tree model was optimized for learning and postpruning. Totally 520 data was verified by a 10fold crossover method to obtain the classification result. The accuracy of GS0~GS4 classification was 66%, 71%, 74%, 98% and 95%, and the overall accuracy was 78%. The above results showed that based on the dynamic multifeature variables extracted from video and decision tree model, the quantitative evaluation of limping state of broilers can be achieved. The research result provided a method for assessing the degree of noncontact broilers with high accuracy. The method can be used as an early detection tool for identification and early warning for broilers limping, which provided support for the realization of farming automation and animal welfare industry upgrading, which had certain practical value.