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Radar target detection has been an important task in radar signal processing in recent years, and it plays a very important role in detection security fields . Aiming at the problems that traditional constant false alarm target detection methods have weak environmental adaptability and the rapid increase in the number of radar false alarms in complex terrain environments, a target detection method based on convolutional neural networks is proposed. The Range-Doppler image obtained by radar echo data processing is used as the training and test set, and the target detection model is designed based on the Faster R-CNN structure. The training and test results are compared with the results of the traditional constant false alarm target detection algorithm. The model improves the accuracy of target detection and greatly reduces the number of false alarms, which shows that it is feasible to apply convolutional neural networks to radar echo signal processing tasks.