As　the　complexity　and　scale　of　systems　continue　to　increase,　enterprises　are　placing　ever　higher　demands　on　system　security,　making　comprehensive　security　analysis　particularly　important.　Among　the　various　methods,　penetration　testing　is　regarded　as　one　of　the　most　direct　means　for　assessing　security.　In　order　to　explore　the　resistance　(to　attacks)　standard　within　the　SQuaRE　series　standards　concerning　product　quality,　this　paper　proposes　a　penetration　path　discovery　method　based　on　an　improved　deep　reinforcement　learning　algorithm,　Adaptive　Soft　Actor-Critic　(ASAC).　This　method　involves　modeling　the　penetration　process　and　quantifying　the　penetration　benefits,　and　it　leverages　the　Soft　Actor-Critic　(SAC)　algorithm,　which　is　suitable　for　complex　state　spaces,　to　construct　an　intelligent　penetration　agent.　The　goal　is　to　solve　for　the　optimal　penetration　path,　thereby　enabling　the　evaluation　of　a　system＇s　resistance　to　attacks　during　system　validation.Experimental　results　demonstrate　that　the　attack　effectiveness　of　the　proposed　ASAC　algorithm　surpasses　that　of　commonly　used　reinforcement　learning　algorithms　such　as　Q-learning　and　DQN.　It　can　quickly　identify　the　most　critical　penetration　paths　in　a　network　and　maintain　high　performance　across　different　network　environments.　This　approach　provides　effective　theoretical　and　technical　support　for　the　comprehensive　assessment　of　the　robustness　of　the　cybersecurity　of　the　system.　©　2024　Copyright　for　this　paper　by　its　authors.