In view of the autonomous air combat maneuver decision-making problem of unmanned aerial vehicles （UAVs）， a decision-making algorithm based on a path-game hybrid strategy is designed. Firstly， according to the principle that horizontal maneuver and vertical maneuver can be decoupled in the flight control process of UAVs， a decoupled autonomous decision-making mechanism is proposed， which uses path planning to realize horizontal maneuver decision-making and adopts game theory to realize vertical maneuver decision-making. In order to improve the flexibility of the decision-making environment， a dynamic grid environment is designed， which can adaptively adjust the planning range and resolution. The path planning model is designed based on Q-learning （QL） algorithm， and the algorithm is improved by using a double Q-table learning mechanism， which effectively improves the quality of path planning. Based on the Nash equilibrium theory， the vertical maneuver algorithm model is constructed， and the cost calculation function is designed according to different situation environments. Thus， the vertical maneuver decision-making of UAVs is realized. Finally， the simulation of a one-to-one air combat confrontation scenario verifies the effectiveness of the algorithm. Compared with the traditional maneuver decision-making based on three-dimensional planning space， the proposed algorithm can effectively shorten the planning time and improve the planning quality.