This paper considers the integrated optimization problem of batch processing and hybrid flow shop scheduling (IOP_BPHFS), which widely exists in the batch chemical companies and automobile manufacturing enterprises. The IO_BPHFS consists of two mutually coupled problems, i.e., the product batch processing subproblem and the batch scheduling subproblem in the hybrid flow shop. Since this problem is NP-hard, a cooperative Q-learning hyper-heuristic evolutionary algorithm (CQHEA) is proposed to address it. In the CQHEA, the batch and production agents execute their specific search actions in their own subspaces, while the joint agent perform its joint search actions in the entire solution space. This collaborative framework enables better exploration in solution space and deeper exploitation in promising regions. Experiment results shows that the proposed algorithm is effective for the considered problem.