Abstract Network function virtualization(NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, we proposes an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C). Firstly, the SFC mapping model is established, the SFC mapping process is divided into two layers of physical topology resource layer and virtual network function request layer, and the two layers are represented by abstract parameters; Secondly, the relationship between mapping resource allocation and service processing process is analysed, and a mathematical model is established for the minimisation of the processing time of the joint allocation of node computation and link bandwidth communication resources, and the minimisation mathematical model is modelled as a Markov process, defining the ternary containing state, action, and reward; Finally, combining the ternary with the deep reinforcement learning algorithm A3C, using the training master network as a template, and using multi-threading technology to generate multiple sub-networks for parallel training to find the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor-Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.