We　propose　a　novel　deep　web　crawling　framework　based　on　reinforcement　learning.　The　crawler　is　regarded　as　an　agent　and　deep　web　database　as　the　environment.　The　agent　perceives　its　current　state　and　submits　a　selected　action　(query)　to　the　environment　according　to　Q-value.　Based　on　the　framework　we　develop　an　adaptive　crawling　method.　Experimental　results　show　that　it　outperforms　the　state　of　art　methods　in　crawling　capability　and　breaks　through　the　assumption　of　full-text　search　implied　by　existing　methods.