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In view of the problem that large-scale Web service composition is difficult to achieve high reliability and high dynamic adaptability in dynamic environment, an adaptive Web service composition method combining priority dual reinforcement learning and POMDP is proposed. Firstly, POMDP is used to model the large-scale Web service composition optimization strategy, which simplifies the steps of composition optimization analysis and improves the efficiency of large-scale Web composite service. Then, on the basis of POMDP, by using dual depth reinforcement learning method, the optimization strategy is restructured and the optimal solution is obtained, which improves the adaptability of composite service to dynamic service environment. The experimental results show that compared with the existing excellent methods, the proposed method has obvious improvement in reliability, efficiency and dynamic environment adaptability.