Abstract-Acute aortic dissection is one of the most life-threatening cardiovascular diseases with a high mortality rate. Its prevalence ranges from 0.2% to 0.8% in humans, resulting in a significant number of deaths due to missed manual examinations. More importantly, the aortic diameter, a critical indicator for surgical selection, significantly influences the outcomes of surgeries post-diagnosis. Therefore, it is an urgent yet challenging mission for an automatic aortic dissection diagnostic system to recognise/classify the aortic dissection type and to measure the aortic diameter. This paper offers a dual-functional deep learning system called DDAsys that enables both accurate classification of aortic dissection and precise diameter measurement of the aorta. To this end, we create a dataset containing 61,190 CTA images from 279 patients from the Division of Cardiothoracic and Vascular Surgery, Tongji Hospital, Wuhan, China. The dataset provides slice-level summary of hard-to-identify features, which helps to improve the accuracy of recognition as well as classification. Our system achieved a classification F1-score of 0.984, and the measurement precision for ascending and descending aortic diameters was 0.994 mm and 0.767 mm Root Mean Square Error respectively. The high consistency (88.6%) between the recommended surgical treatments and the actual corresponding surgeries verifies the capability of the present system to aid clinicians in developing a more prompt, precise, and consistent treatment strategy.