Summary: Using computer vision technology to obtain and analyze biomechanical information is an important research direction in recent years. However, the linear model in the computer vision system cannot accurately describe the geometric relationship of the camera imaging, so it is difficult to realize human posture recognition in high-precision mechanics information. Therefore, how to improve the recognition accuracy is very important. In this paper, we apply nonlinear differential equations to stereo computer vision (SCV) information systems. And based on the median theorem, a nonlinear posture recognition and error compensation algorithm based on BP neural network is proposed to reduce the recognition error. The test set uses the Leeds Motion Pose (LSP) dataset to verify the performance of the algorithm. Experimental results show that the compensated median filter of BP neural network can eliminate glitches in attitude data. Superimposing the output attitude error compensation value with the attitude estimation value can greatly reduce the root-mean-square error of the attitude angle. The result of gesture recognition is closer to reality. Compared with traditional algorithms, the cyclomatic complexity of the proposed BP neural network algorithm has a much lower growth rate in high-order calculations, which indicates that the proposed BP neural network algorithm is more concise and scalable.