As texts generated by video descriptions are of low quality and not novel, this study proposes a codec model based on feature reinforcement and text knowledge supplementation. In the coding stage, the model enhances the fine-grained feature extraction of static objects in a video by strengthening local and global features, thus improving the resolution of similar semantics of objects. Then, it integrates visual semantics and video features into a long short-term memory (LSTM) network. In the decoding stage, to mine the hidden information that can hardly be discovered by machines in the video, the model intercepts partial video frames and detects the visual goals in them. Then, the obtained visual goals are used to extract knowledge from the external knowledge base to supplement the generation of descriptive texts and thus produce more novel and natural text descriptions. The experimental results on datasets MSVD and MSR-VTT demonstrate that the proposed method shows good performance, and the generated content can show novel implicit information to a certain extent.