To address the low capacity-organization efficiency and high cost of the terminal delivery of high-speed railway (HSR) express—in which the delivery process is tightly coupled with the rigid arrival timetable of trains, freight arrives in concentrated batches, and stringent time-sensitive service requirements must simultaneously be satisfied—a collaborative delivery model that integrates unmanned aerial vehicles (UAVs), professional vehicles, and crowdsourcing resources is proposed. The arrival batch at the HSR station is adopted as the dispatching trigger, and a four-tier collaborative delivery network composed of the HSR station, professional vehicles, UAVs, and crowdsourcing couriers is constructed. The train timetable, the post-arrival sorting-and-release time, the latest delivery deadlines of customers, the vehicle capacity, the UAV payload and endurance, and the promised service time and reliability of crowdsourcing couriers are explicitly incorporated as constraints. A bi-objective formulation is established to minimize the total completion time and the total delivery cost, and it is solved by an improved adaptive large neighborhood search (ALNS) algorithm that is equipped with multiple destroy-and-repair operators and an adaptive learning mechanism. Numerical experiments are conducted on instances with 8, 20, and 50 customer nodes around an HSR station in a first-tier city. The results show that, for the 50-node instance, the optimal objective value is 0.972 3, the total completion time is 449.60 min, the total cost is 1 784.13 yuan, and the total delivery distance is 470.89 km; professional vehicles, UAVs, and crowdsourcing couriers serve 29, 9, and 10 customers, respectively, and 100% customer coverage is achieved. It is demonstrated that, while the rigidity of the railway timetable and the batch-arrival characteristics are respected, the elasticity of terminal capacity can be enhanced and a balance between delivery timeliness and cost can be attained by the proposed mode, by which decision support is provided for the organizational optimization of the last mile of HSR express.