Existing methods for video traffic identification are mainly targeted at specific platforms and mostly require capturing full flows, which makes them unsuitable for high-speed networks management. This paper proposes a method for video traffic identification from multi-platforms in the sampled high-speed traffic.This paper analyze multiple video platform transmission protocols, extract features based on their common characteristics to construct a composite feature space, and further process these features to eliminate the effect of sampling on feature stability. Then, feature vectors are extracted and a classification model is trained. In the experiments, high-speed networks traffic with a bandwidth of 10 Gbps and a sampling rate of 1:32 was used. The results showed that the proposed method can quickly identify video traffic from multi-platforms with a precision of over 98%.