As a pivotal technology driving organization digital transformation, Robotic Process Automation (RPA) has garnered significant attention from both the academic and industrial sectors in recent years. However, current deployment strategies suffer from a lack of process analysis, leading to misguided deployment of RPA robots and resource wastage. Furthermore, existing RPA robots deployment methods based on process mining depend overly on domain-specific expertise, limiting their generality. To address these challenges, this study proposes the integration of process mining with RPA robots and presents a deployment method for RPA robots based on process mining. The method is initiated by introducing an approach to mine the global process model from event logs and extracting a Time Petri net containing temporal information. Subsequently, critical process paths are identified using a method designed to recognize key process paths. Finally, an optimization deployment strategy for RPA robots is introduced, which determines the optimal deployment node set considering the time and cost constraints. The proposed method is implemented using ProM, an open-source process mining tool platform. It is compared with four deployment methods in experiments that focus on improving time efficiency. The experimental results indicate that, compared to other deployment methods, this approach results in a time efficiency improvement ranging from 22% to 41%, and the deployment accuracy reaches 1, without relying on domain-specific expert knowledge, validating its generality and accuracy.