In edge computing (EC), services are deployed on edge nodes for lower latency and higher privacy. However, the resource-constrained characteristics of edge servers make services deployment hard to satisfy the quality of service (QoS) requirement. Recently, some research considers deep reinforcement learning (DRL) in service deployment especially for EC. However, these DRL-based service deployment methods ignore the optimization of initial state of DRL and have a low information utilization, which lead to a poor QoS of services. Therefore, we propose a service deployment method, named SDAD, in EC for that integrates association rules and DRL to optimize the initial state and fully utilize information of services and edge nodes. First, we mine association rules about services, edge nodes and QoS from history data. Then, we apply the mined rules to generate a helpful initial service deployment state and to form feature vectors to augment information for DRL. The service deployment is finally evolved through DRL. The experimental results show that our SDAD has a 6.4% improvement over SOTA (state-of-the-art) in total running time and average waiting time.