To support the rapid switching of unit control modes in grid-following/grid-forming hybrid new energy stations and achieve safe and stable operation of these stations that can adapt to changes in grid strength, a rapid assessment method for small-signal stability of grid-connected systems in grid-following/grid-forming hybrid new energy stations based on temporal convolutional network (TCN) is proposed. Specifically, an aggregated impedance model for grid-following/grid-forming hybrid new energy stations is constructed, and the small-signal stability margin of the grid-connected system is obtained through eigenvalue calculations. Furthermore, using the short-circuit ratio of the grid-connected system and the information on the grid-following/grid-forming control mode of the new energy station as input features, and the small-signal stability margin and damping ratio of the grid-connected system as output features, a TCN is trained to obtain a rapid assessment model for small-signal stability of grid-connected systems in hybrid new energy stations. The trained model can quickly output the corresponding small-signal stability margin and damping ratio based on the short-circuit ratio and the control mode of each unit in the grid-following/grid-forming hybrid new energy station. A case study is conducted using a new energy station with 10 wind turbines, and the results show that compared to the long short-term memory neural network method, the proposed method reduces the mean absolute percentage error of small-signal stability margin prediction and damping ratio prediction by 16.76% and 14.75% respectively. Additionally, the computation time of the proposed method is reduced by 98.54% compared to the eigenvalue calculation method, verifying the accuracy and efficiency of the proposed rapid assessment method for small-signal stability.