The miscellaneity of grid information in the existing target tracking algorithm is high, which increases the complexity of the tracking algorithm and makes the calculation amount of tracking too large, which affects the tracking effect. Therefore, the primary goal is reducing the complexity of the tracking process. In this paper, under the condition of considering the feature constraints, the optical flow field model is used to achieve the effective tracking of multi-channel video targets. The video random variable is set to control the variation of the numerical model of optical flow field, so as to control the deviation and extract the normalized features of multi-channel video, and the global pooling processing method is introduced to solve the problem of complex calculation of multi-dimensional features in the traditional method. Firstly, according to the numerical relationship between image pixel intensity and time parameters, the deviation of the mean value of optical flow field model is controlled to obtain video features. Then, the activation function is used to map the multi-channel parameters into fixed parameters, and the multi-channel feature constraint relationship is constructed to control the increasing dimension of the feature parameters and reduce the calculation dimension of the data. Finally, the background and target areas in the video image are divided, and the video target is tracked by constructing the pixel motion model. After setting the initial parameters of the experimental instrument, the video clips are randomly selected from the database as the experimental object, and the image resolution of different frames is set to simulate them as multi-channel video information. The performance tests of the traditional algorithm and the proposed algorithm are carried out, and the results show that the algorithm in this paper can not only guarantee the tracking accuracy higher than 0.90, but also control the successful tracking rate above 96%, and the tracking process takes only 5.5 s at most. The algorithm in this paper can track multi-channel video target with low computational complexity and has low computational cost.