With the rapid development of urban rail transit network in China, the number of lines and passenger demand are increasing sharply day by day. The coordination between lines brings great challenges to the operation due to its high complexity. In order to minimize the unsuccessful transfer-passenger flow transfer of last-train under worse case with given level of tolerance, a distributionally robust chance-constrained programming model is proposed for the last-train connection planning problem in urban rail transit networks under uncertain transfer-passenger demand, in which the probability distribution of uncertain parameters is only partially known. By analyzing the relationship between the distributionally robust optimization model and the traditional robust optimization model, it is proved that the former is an extension of the latter. Furthermore, the original model is reformulated into a second-order mixed-integer conic programming form which can be solved by CPLEX under the Gaussian perturbations ambiguity set. The results of a numerical case indicate that the proposed model can be solved to optimality quickly by CPLEX, and can effectively avoid over-conservative optimization compared to the classic robust optimization model and reduce unsuccessful transfer-passenger flow in the worse case compared with the traditional stochastic programming model, which exhibits more robust performance.