Due to the need of extracting skeleton information and gait parameters for walking quadruped, a skeleton extraction model of quadruped is proposed by using context information enhancement and multi-scale information fusion based on HRnet, and a quantitative analysis method for gait parameters is established. The validity of the model is verified on the test data set of images. Experiments show that, in the key point estimation of quadruped, the model achieves good performance with the mean similarity of 81. 04%, the accuracy of 92. 77%, and the recall rate of 92. 75%. Based on the skeleton extraction model, the frequency of buffalo and alpaca were analyzed and calculated. Compared with the manual statistical results, the maximum relative error was 2. 73%. Through analyzing the angle variations of buffalo’ s and alpaca’ s hip and knee joints during a complete gait cycle respectively, the joint motion logic and gait sequence of walking quadruped can be extracted automatically. Finally, taking the rhinoceros as a sample, it is demonstrated that the proposed method can work adaptively in a range of different shooting angle of images. The results can provide a reference for intelligent perception of quadruped motion information.