Abstract: Research on knowledge-grounded dialogue often suffers from the problem of external knowledge containing redundant or even noisy information irrelevant to the conversation topic, which leads to a degradation in the performance of the dialogue system. Knowledge selection becomes an important approach to solving this issue. However, existing work has not yet investigated in depth some issues involving it such as how to design a knowledge selector, how to exploit the selected knowledge, what are the suitable scenarios for the knowledge selection conversation methods, etc. In this paper, we propose a new neural conversation method based on conditional variational attention knowledge selection and a pre-trained language model. This method employs a knowledge selection algorithm based on CVAE and a multi-layer attention mechanism to pick up the most relevant textual knowledge collection to the current conversation, which effectively exploits the dialogue response in training data to improve the efficiency of knowledge selection. Our novel model adopts the pre-trained language model Bart as encoder-decoder architecture and incorporates selected textual knowledge into the Bart model to fine-tune it during the training process. The experimental results show that the model proposed, in contrast to the current representative dialog models, can generate more diverse and coherent dialogue responses with higher accuracy.