This　article　develops　a　novel　data-driven　safe　Q-learning　method　to　design　the　safe　optimal　controller　which　can　guarantee　constrained　states　of　nonlinear　systems　always　stay　in　the　safe　region　while　providing　an　optimal　performance.　First,　we　design　an　augmented　utility　function　consisting　of　an　adjustable　positive　definite　control　obstacle　function　and　a　quadratic　form　of　the　next　state　to　ensure　the　safety　and　optimality.　Second,　by　exploiting　a　pre-designed　admissible　policy　for　initialization,　an　off-policy　stabilizing　value　iteration　Q-learning　(SVIQL)　algorithm　is　presented　to　seek　the　safe　optimal　policy　by　using　offline　data　within　the　safe　region　rather　than　the　mathematical　model.　Third,　the　monotonicity,　safety,　and　optimality　of　the　SVIQL　algorithm　are　theoretically　proven.　To　obtain　the　initial　admissible　policy　for　SVIQL,　an　offline　VIQL　algorithm　with　zero　initialization　is　constructed　and　a　new　admissibility　criterion　is　established　for　immature　iterative　policies.　Moreover,　the　critic　and　action　networks　with　precise　approximation　ability　are　established　to　promote　the　operation　of　VIQL　and　SVIQL　algorithms.　Finally,　three　simulation　experiments　are　conducted　to　demonstrate　the　virtue　and　superiority　of　the　developed　safe　Q-learning　method.