One challenge in Natural Language Processing (NLP) area is to learn semantic representation in different contexts. Recent works on pre-trained language model have received great attentions and have been proven as an effective technique. In spite of the success of pre-trained language model in many NLP tasks, the learned text representation only contains the correlation among the words in the sentence itself and ignores the implicit relationship between arbitrary tokens in the sequence. To address this problem, we focus on how to make our model effectively learn word representations that contain the relational information between any tokens of text sequences. In this paper, we propose to integrate the relational network(RN) into a Wasserstein autoencoder(WAE). Specifically, WAE and RN are used to better keep the semantic structurse and capture the relational information, respectively. Extensive experiments demonstrate that our proposed model achieves significant improvements over the traditional Seq2Seq baselines.