The split feasibility problem (SFP) is finding a point such that , where C and Q are nonempty closed convex subsets of Hilbert spaces and , and is a bounded linear operator. Byrne’s CQ algorithm is an effective algorithm to solve the SFP, but it needs to compute , and sometimes is difficult to work out. López introduced a choice of stepsize , , . However, he only obtained weak convergence theorems. In order to overcome the drawbacks, in this paper, we first provide a regularized CQ algorithm without computing to find the minimum-norm solution of the SFP and then obtain a strong convergence theorem.