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There are huge amounts of User Generated Contents (UGCs) consisting of authors’ articles of different themes and readers’ on-line comments on social networks every day. Generally, an article often gives rise to thousands of readers’ comments, which are related to specific points of the originally published article or previous comments. Hence it has suggested the urgent need for automated methods to implement the content linking task, which can also help other related applications, such as information retrieval, summarization and content management. So far content linking is still a relatively new issue. Because of the unsatisfactory of traditional ways based on feature extraction, we look forward to using deeper textual semantic analysis. The Word Embedding model based on deep learning has performed well in Natural Language Processing (NLP), especially in mining deep semantic information recently. Therefore, we study further on the Word Embedding model trained by different neural network models from which we can learn the structure, principles and training ways of the neural network language model in more depth to complete deep semantic feature extraction. With the aid of the semantic features, we expect to do further research on content linking between comments and their original articles from social networks, and finally verify the validity of the proposed method by comparison with traditional ways based on feature extraction.