The emergence of network function virtualization (NFV) technology allows network functions are provided by virtual network functions (VNFs) to improve network flexibility, scalability and cost-effectiveness. However, an important challenge for NFV is how to efficiently place VNFs in different network locations and chain them to steer traffic while minimizing energy consumption. In addition, in the face of network quality of service requirements, improving the service acceptance rate is also critical to network performance. To address these issues, in this study we investigate VNF placement and chaining (VNFPC) in NFV to maximize the service acceptance rate while optimizing the energy consumption trade-off. Therefore, an energy-efficient VNFPC method based on Actor-Critic deep reinforcement learning (DRL), called ACDRL-VNFPC, is designed in NFV. The approach applies adaptive sharing scheme to achieve energy savings by sharing the same type of VNFs among multiple services and sharing the same server among multiple VNFs. The experiment results show that the proposed algorithm effectively trades off the energy consumption and service acceptance rate, and the execution time is also optimized. Compared with the baseline algorithm, ACDRL-VNFPC improves the performance in terms of service acceptance rate, energy consumption and execution time by 2.39%, 14.93% and 16.16%, respectively.