The full-scale accelerated loading test field has a complex pavement structure, in which a variety of sensors are embedded to monitor indicators of pavement performance. For the high-frequency and massive data collected by the sensors, the identification of abnormal data using traditional methods has low efficiency and poor accuracy. Considering this, this study visualizes the originally collected high-frequency data through specific software and then labels the visualized data as the original dataset. Next, according to the characteristics of obvious shape features of the data after visualization, the lightweight convolutional neural network model GhostNet is selected to automatically identify the abnormal data from the monitored dataset by sensors. Through the parameter design and the network model training, the test results on the verification set show that the identification rate of abnormal data is as high as 99%. Compared with the conventional classification model residual neural network (Resnet50), the GhostNet model has improved the anomaly identification accuracy by 11%. It can quickly identify abnormal data in massive monitored data by pavement sensors, which can provide strong data support for pavement sensor fault monitoring.