Unmanned aerial vehicle （UAV）-mounted base stations possess characteristics， such as rapid deployment and flexible coverage， making them an effective solution for emergency communication in railways. However， the low-altitude communication network formed by UAVs faces the constraint of the limited energy storage and the risk of data eavesdropping or tampering. This paper introduces blockchain technology into a UAV-assisted railway wireless communication system to ensure data security. Considering the constraints on transmission delay and data queue stability， this paper proposes a joint optimization problem aimed at minimizing the energy consumption of the UAV-assisted communication system and the latency of the blockchain. To solve this non-convex， mixed-integer， and time-varying stochastic optimization problem， a Lyapunov-based drift-plus-penalty method is proposed to transform the long-term stochastic optimization problem into sub-problems of multiple time slots. Deep reinforcement learning based on D3QN-TD3 is designed to solve these sub-problems， and then the optimal association strategies and power control for each time slot are obtained. Experimental results demonstrate the significant effectiveness of the proposed method in reducing the energy consumption and delay.