When reading an article, especially a professional article, we often encounter words or phrases that we don't recognize. They may be specific domain terms or emerging entities. When we can't guess their meaning from the article, we generally refer to the terminology dictionary or search for related content on the Internet to understand them. Some researchers have used natural language generation (NLG) models to explain these unknown phrases in recent years automatically. Still, current NLG models have difficulties generating long sentences with good coherence, and they are difficult to generate multiple sentences that describe unknown phrases from different angles. Therefore, this paper proposes a model that can judge whether an existing sentence can explain a certain phrase, called TransExplain. TransExplain can use LSTM, convolutional neural network, and attention mechanism to extract multiple sentence features and map them to a fixed-dimensional semantic feature vector. By calculating the cosine similarity between the semantic feature vector and the unknown phrase vector, it is judged whether the sentence can explain the semantics of the unknown phrase. And a loss function called positive and negative means square error is introduced to improve the model's ability that distinguishes negative examples. For this task, we provided a Chinese dataset containing phrases and explanation pairs in 7 important domains. On this dataset, TransExplain can achieve better results than previous similar tasks.