In multi-UAVs system, high-quality communication system is the key to ensure cooperation. Most of the existing methods focus on the trade-off between connectivity and the number of network edges. However, for a multi-UAVs communication network with high quality of service (QoS), more metrics of communication network need to be considered, such as the hops and the maximum number of links. In this article, we propose a multi-objective optimization method for dynamic communication network. Firstly, in order to accurately estimate the moved positions with random noise, Long and Short Term Memory network (LSTM) is used to predict succeeding positions of all UAVs. Secondly, Deep Q-network (DQN) with random action space is designed to adaptively adjust the communication network. In particular, it can be applied to large-scale communication networks. Furthermore, we propose a transfer learning method based on the trained LSTM and DQN. In this transfer learning, these two models which are well-trained in smaller communication network and combined with principal component analysis (PCA) can be directly used for communication network optimization of larger networks. Experimental results show that our method is not only superior to other methods but also faster in convergence.