This paper proposes an improved data-driven strategy for aircraft control systems, encompassing three aspects: data-driven identification, data-driven control, and data-driven validation. First, after reviewing the classical aircraft flight control system as a closed-loop system with an unknown aircraft model and controller, within the framework of perfect tracking, the controller is derived by solving an optimization problem. To provide an explicit form of the cost function, data-driven identification is applied to construct an auxiliary model, replacing the unknown aircraft model. Secondly, to avoid the modeling process, data-driven control is applied to design a parameterized control, whose controller parameters are tuned using a gradient algorithm. Thirdly, to verify whether the designed controller performs well, data-driven validation is applied to the correlation-based validation method. Finally, detailed derivations of our proposed data-driven strategy are provided from a theoretical perspective, and its application in aircraft flight systems is also presented. Overall, this paper combines our new contributions from the theory of data-driven strategies with their practical application.