HGCAL is a key component of the next phase of the CMS upgrade. This study aims to monitor potential anomalies during HGCAL operation using machine learning methods. Due to the unique hexagonal layout of HGCAL, conventional convolutional neural network (CNN) algorithms are no longer directly applicable. I have innovatively applied the Radial Distribution Function (RDF) to address this challenge, achieving better results than traditional approaches. This work may offer new perspectives for future applications of machine learning in high energy physics