The invention provides a depth signal detection method for a high-speed rail, and the method comprises the steps: firstly, collecting data, and collecting a plurality of sending signals and receivingsignals in each scene along the high-speed rail according to different environment types along the high-speed rail; secondly, dividing scenes, and further dividing each scene into a plurality of regions through data analysis to meet the compatibility of the neural network; thirdly, establishing a deep high-speed rail signal detection neural network model; secondly, training a high-speed rail signal detection neural network offline; and finally, carrying out online real-time signal detection, determining the position information of the high-speed rail through a GPS in the driving process of thehigh-speed rail, judging the area where the high-speed rail is located, selecting a corresponding neural network model, then inputting signals received in real time into the trained neural network, and outputting signals sent by the base station end in real time. The system performance is greatly improved, the signal detection bit error rate is reduced, and the algorithm is more robust. The method used in the invention does not need to estimate the channel, thereby saving the pilot cost.