To address the problem of low allocation efficiency caused by the strong coupling of power, bandwidth and timeslot resources in low earth orbit (LEO) satellite beam hopping systems, which further restricts the improvement of user satisfaction, this paper proposes a joint power-bandwidth allocation scheme for LEO satellite beam hopping systems based on Rainbow deep Q-network (DQN) deep reinforcement learning. First, a multi-dimensional state space is constructed by comprehensively considering features such as channel state information, queue state information and service requirements, and a joint optimization model for timeslot, power and bandwidth resources is established with the goal of maximizing user satisfaction. Second, considering the variable user demands and user states in the LEO satellite environment, the DQN is adopted for timeslot allocation learning, and the trust region interior point method is used to output power allocation to meet user requirements. Finally, to achieve and accelerate the convergence of the algorithm in the task, the Rainbow DQN algorithm is introduced in this paper.By integrating the optimization characteristics of multiple technologies, it rapidly generates the joint timeslot-power-bandwidth allocation strategy for the LEO satellite system. Simulation results show that the proposed resource allocation scheme can optimize the joint power and bandwidth configuration of the LEO satellite beam hopping system, improve user satisfaction and algorithm training stability, and effectively verify the application advantages of multi-technology fusion reinforcement learning in discrete action spaces.