High-speed trains powered by electricity have low-carbon emissions and are the important intercity travel conveyances conducive to sustainable social development. The sustainable development of high-speed railway needs to improve the operating revenue and the resource utilization. Joint optimization of dynamic pricing and ticket allocation are often used to improve high-speed railway revenue and seat utilization in the literature. However, the uncertainty of demand makes joint decision-making difficult to be accurate. The risk preference of decision-makers also deeply affects the effect of joint decision-making. To cope with the uncertain demand and test the effect of operator risk preference on joint decision-making, this paper used chance constrained programming theory to optimize dynamic pricing and ticket allocation simultaneously. The presale period is divided into several stages. In each stage, ticket demand follows the normal distribution and changes with the price elasticity. Passengers choose tickets according to the multinomial logit model. The chance constrains reflect operators’ risk preference. The chance constrained stochastic nonlinear programming is proposed for joint decision of dynamic pricing and ticket allocation. Then, the combination algorithm of particle swarm optimization algorithm and mixed-integer linear programming was designed to solve the model. Finally, the numerical experiments according to actual operating scale were design to validate the model and algorithm. The results indicate that under different confidence levels, the proposed model and algorithm increase the total revenue by 11.84%-13.40% compared with the ticket allocation under the single fixed fare. The model can help the high-speed railway operators understand the impact of risk preference on joint decision-making, and provide decision support for them.