As the scheduling problem with wide application backgrounds, the research of intelligent algorithms for flow-shop scheduling is of important academic significance and application value. With the criterion of minimizing the maximum completion time, a framework is proposed based on deep reinforcement learning and the iterative greedy method for solving the permutation flow-shop scheduling. Firstly, a new encoding network is designed to model the problem to avoid the defect in generalizing the classic model affected by problem scale, and the reinforcement learning is used to train the model to yield good output result. Then, an iterative greedy algorithm with feedback mechanism is proposed by using the output result of the trained model as the initial solution. Multiple local search operators are conducted in a collaborative way and adjusted their utilizations according to the feedback of performances for obtaining the final schedule. Simulation results and statistical comparisons show that the proposed algorithm fusing deep reinforcement learning and the iterative greedy method is able to achieve better performances.