Deep neural networks have displayed promising performance in various fields, including biometrics, medical image processing and analysis, as well as dental healthcare. However, deep learning solutions have not yet become the norm in routine dental practice. This is mainly due to the scarcity of dental datasets. To address this challenge, we have built a dataset called Quadruple Dental X-ray Panoramic (Quad-DXP) Dataset, specifically targeted at the recognition of dental disease and treatment. This dataset annotates nine types of dental issues (disease or treatment), and is the dental panorama dataset with the most abundant types of annotations so far. We further propose a framework for dental pathological issue identification on panoramic radiographs. This framework takes a panoramic X-ray image as input, feeds it into a series of neural network modules, and then achieves the recognition results of dental disease/treatment and enumeration detection. We have achieved satisfactory experimental results under the supervision of dentists and experts, which proves the effectiveness and reliability of our framework in dental diagnosis. This work can assist dentists in formulating treatment plans and improving dental healthcare.