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Aiming at the problem that the doubly-selective fading and non-stationary characteristics of the Orthogonal Frequency Division Multiplexing (OFDM) system in the high-speed mobile environment lead to the limited performance of the downlink channel estimation, a basis extension model (BEM) combined with deep learning (DL) channel estimation method. Aiming at the doubly-selective fading characteristics of the channel in the high-speed mobile environment, BEM is used to model the channel, which converts the estimation of a large number of channel impulse responses into a small number of basis coefficients, which reduces the parameters to be estimated and effectively reduces the complexity of channel estimation. Aiming at the non-stationary characteristics of the channel in the highly mobile environment, a deep neural network is established, and the change characteristics of the doubly-selective channel are learned through offline training, which improves the accuracy of channel estimation. The simulation results show that in the highspeed mobile environment, com-pared with the traditional methods, the proposed channel estimation method has significant perfor-mance improvement.