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Video semantic segmentation is a challenging vision task since the temporal-spatial characteristics are difficult to model to satisfy the requirements of real-time and accuracy simultaneously. To tackle this problem, this paper proposes a novel optical flow based method. We propose an adaptive threshold key frame scheduling strategy to model the temporal information by estimating the inter-frame similarity. To ensure segmentation accuracy, we construct a convolutional neural network named Quick Network with attention (QNet-attention), a lightweight image semantic segmentation model with a spatial-pyramid-pooling-attention module. The proposed network is further combined with optical flow estimation to realize a semantic segmentation framework. The performance of the proposed method is verified with existing benchmark methods. The experimental results indicated that our method achieves excellent balanced performance on accuracy and speed.