The core problem of penetration test is the planning of penetration test paths. Manual planning relies on the experience of testers， while automated generation of penetration paths is mainly based on the priori knowledge of network security and specific vulnerabilities or network scenarios， which requires high cost and lacks flexibility. To address these problems， a reinforcement learning-based penetration path recommendation model named Q Learning Penetration Test （QLPT） was proposed to finally give the optimal penetration path for the penetration object through multiple rounds of vulnerability selection and reward feedback. It is found that the recommended path of QLPT has a high consistency with the path of manual penetration test by implementing penetration experiments at open source cyber range， verifying the feasibility and accuracy of this model； compared with the automated penetration test framework Metasploit， QLPT is more flexible in adapting to all penetration scenarios.