When using the existing search engines, users often fail to construct clear and accurate query words, which leads to poor retrieval results. Traditional query recommendation methods do not fully consider the relevance of user behavior, resulting in inaccurate query recommendation results. This study builds a new query recommendation model, which is based on the click model and network embedding. Firstly, the model embeds the user’s history view behavior and click behavior through the click chain model and measures the relevance between the query and the returned documents through the attention mechanism; secondly, it uses the attribute heterogeneous network to obtain the potential semantic information in a complex heterogeneous network structure; finally, it captures the complex information in multiple spaces through multi-head attention and uses multi-task learning to make score prediction. The experimental results on the public query log provided by SogouLabs show that our model is superior to the baseline model in both discriminative and generative tasks.