Based on the fact that most steganography approaches follow the rules of minimum distortion or maximum entropy，this paper proposed to employ the distribution of optimal embedding probability as the prior knowledge for video steganalysis.To better characterize the embedding priority of motion vectors，it defined a measurement of the embedding distortion of motion vectors using features from three aspects，namely motion feature，texture feature and local optimality under coding framework，and the embedding probabilities of motion vectors were estimated with Gibbs distribution.Thus this study proposed a way of quantitatively enhancing the steganalytic features with the embedding probabilities and the mechanism of the enhancement was explained from the perspective of relative entropy.Experimental results show that the detection accuracy of three classical steganalytic methods have been unanimously improved and show robustness against different bitrates after the enhancement with the proposed method.The effectiveness of the new method is also verified by the comparison with the latest deep neural network VSRNet detection method.