[Purpose/significance] Topic knowledge tuple is a knowledge unit for operating and managing knowledge oriented to knowledge themes. Accurately extracting topic knowledge tuples facilitates the storage, expression and retrieval of knowledge, and realizes knowledge creation and knowledge evaluation in the process of using knowledge. Therefore, this article discusses the existing extraction methods and then, by taking car products as an example, comes up with a method of extracting topic knowledge tuples from professional social media.[Method/process] First of all, this paper extracted a theme list from the users' comments in car forums with the LDA model. Secondly, based on the deep learning model T-LSTM which integrated thematic features, a sentiment analysis model suitable for the corpus of users in car forums was built. Then, by calculating the importance of each word in the TextRank diagram model and the similarity of each word's Word2Vec topic, we extracted key words and key sentences for the purpose of interpreting the extracted theme and sentiment orientation. Finally, the above methods were encapsulated into an integrated topic knowledge tuple extraction method.[Result/conclusion] In the experimental results, the qualification rate of extracted topic knowledge tuples reaches 69.1%. Experimental results show that the proposed method in this paper is capable of refining and extracting each element of knowledge tuples around the topic, meanwhile it can transforms unstructured information into structural knowledge.