The invention relates to a virtualized network service function chain deployment method based on deep reinforcement learning, which is used for solving the problem of virtualized network service function deployment under an edge computing background, and belongs to the technical field of edge computing. According to the method, the two problems of virtual function placement and flow routing are solved respectively, deployment of a service function chain with the minimum cost is achieved, and the advantages of deep reinforcement learning can be utilized to meet the flow control requirement changing along with time. According to the method, a neural network is used as a basis for accumulating the reward Q value. In addition, when a sample is input into the neural network, the concept of an experience pool is introduced into deep reinforcement learning. According to the method, the total cost and the end-to-end delay, especially the intermediate processing delay, are considered, and the method is suitable for being applied to a dynamic and complex scene with high requirements for the communication cost and delay of the server.