Compliance checking on earthquake-resistance is essential for architectures, especially for high-rise buildings. Current checking methods rely heavily on human efforts. In particular, as one of the critical checking contents, the identification of the plane irregularity of structures is time-consuming and error pone, because the building plane designs are becoming increasingly complex. The identification of the plane irregularity of structures could be regarded as a plane classification problem where the regular planes are identified as normal samples and the irregular ones as abnormal samples. Considering the unbalanced distribution of regular and irregular planes in construction projects and adopting the idea of an anomaly detection model, a methodology for the identification of the plane irregularity was proposed based on Building Information Modeling (BIM) and deep learning. Firstly, the building plane of BIM model was obtained by Boolean intersection operation between geometric objects. After image processing, the building plane could be converted into a building plane contour map. Finally, the trained anomaly detection model was executed on the contour map to yield the identification results. The experimental results show that in comparison with the traditional image classification models, the new one following the idea of an anomaly detection model can increase the identification rate of irregular building planes by 15%, more readily meeting the needs of practical applications.