In recent years, China’s high-speed train sets have developed rapidly, with numerous trains operating across the nation’s “Eight Vertical and Eight Horizontal” high-speed rail network, posing significant challenges for train operation, maintenance, and inspection. This paper establishes a defect detection plan based on subway bogie inspection standards, conducting inspection and analysis on components such as the axle box front cover and anti-roll torsion bar. Following the general bogie defect detection process, the inspection plan designed by the operation depot is implemented. A defect detection model is constructed using a convolutional neural network (CNN) algorithm. Through a three-stage detection process, defect detection in the bogie region is accomplished. Performance metrics are used to quantify the model’s performance on the test dataset. On the test set v2, the predicted values for the three metrics—MAE, MAPE, and RMSE at a 5-step length—are 1.1125, 3.0421, and 1.9866, respectively, outperforming other models. Simultaneously, the model maintains tracking sensitivity above 90% during train emergency braking scenarios, demonstrating its high prediction accuracy.