In this paper, the decentralized control problem is solved based on a policy iteration algorithm for large-scale nonlinear systems with unknown mismatched interconnections. The unknown interconnection is approximated by a neural network with local states of isolated subsystem and substituted reference states of coupled subsystems. Then, an adaptive estimation term is utilized to construct the improved local performance index function that reflects the substitution error. Hereafter, the closed-loop large-scale nonlinear system is guaranteed to be ultimately uniformly bounded by the implementation of a set of developed decentralized optimal control policies. Two simulation examples are given to verify the effectiveness of the presented scheme. The significant contribution of this scheme lies in that it removes the common assumptions on satisfying matching condition and upper boundedness of interconnections, when designing the decentralized optimal control for large-scale nonlinear systems.