This　article　aims　to　achieve　data-based　online　evolving　control　for　zero-sum　games　with　unknown　dynamics.　First　of　all,　the　value-iteration-based　Q-learning　framework　is　established.　Relevant　properties　of　the　iterative　Q-learning　framework　are　analyzed,　including　the　convergence　and　monotonicity.　Then,　the　stability　property　is　investigated　and　the　online　data　is　employed　for　off-policy　learning.　More　importantly,　two　effective　algorithms　are　designed　to　achieve　online　evolving　control.　In　one　algorithm,　the　monotonically　nondecreasing　Q-learning　sequence　requires　the　admissible　criterion　to　guarantee　the　stability　with　the　simple　Q-function　initialization.　In　another　algorithm,　the　monotonically　nonincreasing　Q-function　sequence　can　ensure　the　stability　without　the　admissible　criterion,　but　it　requires　an　elaborate　initial　Q-function.　In　the　end,　by　including　two　examples　of　real　physical　backgrounds,　the　excellent　performance　of　online　evolving　control　is　exhibited　with　the　given　algorithms.