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In response to the challenges of complex disease identification and the underutilization of multi-view radar image features in the classification algorithm for roadbed diseases, this paper proposes an intelligent classification method for roadbed diseases based on an improved GoogleNet-ResNet algorithm. Firstly, it introduces coordinate attention and improved Inception modules to optimize the GoogleNet network structure. Then, the improved GoogleNet is utilized to learn the c-scan data features, eliminating non-target diseases and achieving a coarse classification of the disease targets. Finally, the b-scan data classified as diseases is input into a ResNet50 model based on transfer learning to achieve the fine classification of the diseases. The results show that the improved GoogleNet achieves an accuracy of 98.2% for coarse disease classification and a detection speed of 90.9 FPS. The accuracy of disease sub-classification using ResNet50 based on transfer learning reaches 90.5%, and the detection speed reaches 52.6 FPS. The proposed algorithm in this paper achieves an accuracy that is 10.1% higher than that of the improved GoogleNet network alone, and 7.4% higher than that of the ResNet50 network alone. This algorithm effectively improves the recognition accuracy and efficiency of roadbed disease detection while reducing the probability of misjudgments.